Journal articles on the topic 'Breast cancer prediction'
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Khubchandani, Pratham, Harshith Deeti, and Beeram Harsh. "Breast Cancer Prediction in Python." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 274–77. http://dx.doi.org/10.22214/ijraset.2022.47870.
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Abstract: Breast maximum cancers is maximum cancers that office work withinside the cells of the breasts. After pores and pores and skin maximum cancers, breast maximum cancers is the most common location maximum cancers identified in girls withinside the United States. Breast maximum cancers can get up in every men and girls, but it's far more now no longer unusualplace in girls. Substantial resource for breast maximum cancers popularity and research funding has helped created advances withinside the evaluation and treatment of breast maximum cancers. Breast maximum cancers survival costs have increased, and the amount of deaths associated with this sickness is regularly declining, in big element due to factors along with earlier detection, a state-of-the-art custom designed approach to treatment and a better data of the sickness. Machine reading (ML) is a form of artificial intelligence (AI) allowing program application applications to emerge as more accurate at predicting results without the need of programmed to do so. Machine reading algorithms use anciental records as input to anticipate new output values.
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Assegie, Tsehay Admassu, R. Lakshmi Tulasi, and N. Komal Kumar. "Breast cancer prediction model with decision tree and adaptive boosting." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (March 1, 2021): 184. http://dx.doi.org/10.11591/ijai.v10.i1.pp184-190.
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In this study, breast cancer prediction model is proposed with decision tree and adaptive boosting (Adboost). Furthermore, an extensive experimental evaluation of the predictive performance of the proposed model is conducted. The study is conducted on breast cancer dataset collected form the kaggle data repository. The dataset consists of 569 observations of which the 212 or 37.25% are benign or breast cancer negative and 62.74% are malignant or breast cancer positive. The class distribution shows that, the dataset is highly imbalanced and a learning algorithm such as decision tree is biased to the benign observation and results in poor performance on predicting the malignant observation. To improve the performance of the decision tree on the malignant observation, boosting algorithm namely, the adaptive boosting is employed. Finally, the predictive performance of the decision tree and adaptive boosting is analyzed. The analysis on predictive performance of the model on the kaggle breast cancer data repository shows that, adaptive boosting has 92.53% accuracy and the accuracy of decision tree is 88.80%, Overall, the adaboost algorithm performed better than decision tree.
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Kumari, Madhu, and Vijendra Singh. "Breast Cancer Prediction system." Procedia Computer Science 132 (2018): 371–76. http://dx.doi.org/10.1016/j.procs.2018.05.197.
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Das, Akhil Kumar, Saroj Kumar Biswas, and Ardhendu Mandal. "An Expert System for Breast Cancer Prediction (ESBCP) using Decision Tree." Indian Journal Of Science And Technology 15, no. 45 (December 5, 2022): 2441–50. http://dx.doi.org/10.17485/ijst/v15i45.756.
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Kumar, Mukesh, Saurabh Singhal, Shashi Shekhar, Bhisham Sharma, and Gautam Srivastava. "Optimized Stacking Ensemble Learning Model for Breast Cancer Detection and Classification Using Machine Learning." Sustainability 14, no. 21 (October 27, 2022): 13998. http://dx.doi.org/10.3390/su142113998.
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Breast cancer is the most frequently encountered medical hazard for women in their forties, affecting one in every eight women. It is the greatest cause of death worldwide, and early detection and diagnosis of the disease are extremely challenging. Breast cancer currently exceeds all other female cancers, including ovarian cancer. Researchers can use access to healthcare records to find previously unknown healthcare trends. According to the National Cancer Institute (NCI), breast cancer mortality rates can be lowered if the disease is detected early. The novelty of our work is to develop an optimized stacking ensemble learning (OSEL) model capable of early breast cancer prediction. A dataset from the University of California, Irvine repository was used, and comparisons to modern classifier models were undertaken. The implementation analyses reveal the unique approach’s efficacy and superiority when compared to existing contemporary categorization models (AdaBoostM1, gradient boosting, stochastic gradient boosting, CatBoost, and XGBoost). In every classification task, predictive models may be used to predict the class level, and the current research explores a range of predictive models. It is better to integrate multiple classification algorithms to generate a set of prediction models capable of predicting each class level with 91–99% accuracy. On the breast cancer Wisconsin dataset, the suggested OSEL model attained a maximum accuracy of 99.45%, much higher than any single classifier. Thus, the study helps healthcare professionals find breast cancer and prevent it from happening.
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Xiong, Wei, Neil Yeung, Shubo Wang, Haofu Liao, Liyun Wang, and Jiebo Luo. "Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Autoencoders." BME Frontiers 2022 (April 7, 2022): 1–10. http://dx.doi.org/10.34133/2022/9763284.
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Objective and Impact Statement. We adopt a deep learning model for bone osteolysis prediction on computed tomography (CT) images of murine breast cancer bone metastases. Given the bone CT scans at previous time steps, the model incorporates the bone-cancer interactions learned from the sequential images and generates future CT images. Its ability of predicting the development of bone lesions in cancer-invading bones can assist in assessing the risk of impending fractures and choosing proper treatments in breast cancer bone metastasis. Introduction. Breast cancer often metastasizes to bone, causes osteolytic lesions, and results in skeletal-related events (SREs) including severe pain and even fatal fractures. Although current imaging techniques can detect macroscopic bone lesions, predicting the occurrence and progression of bone lesions remains a challenge. Methods. We adopt a temporal variational autoencoder (T-VAE) model that utilizes a combination of variational autoencoders and long short-term memory networks to predict bone lesion emergence on our micro-CT dataset containing sequential images of murine tibiae. Given the CT scans of murine tibiae at early weeks, our model can learn the distribution of their future states from data. Results. We test our model against other deep learning-based prediction models on the bone lesion progression prediction task. Our model produces much more accurate predictions than existing models under various evaluation metrics. Conclusion. We develop a deep learning framework that can accurately predict and visualize the progression of osteolytic bone lesions. It will assist in planning and evaluating treatment strategies to prevent SREs in breast cancer patients.
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Pankratz, V. Shane, Amy C. Degnim, Ryan D. Frank, Marlene H. Frost, Daniel W. Visscher, Robert A. Vierkant, Tina J. Hieken, et al. "Model for Individualized Prediction of Breast Cancer Risk After a Benign Breast Biopsy." Journal of Clinical Oncology 33, no. 8 (March 10, 2015): 923–29. http://dx.doi.org/10.1200/jco.2014.55.4865.
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Purpose Optimal early detection and prevention for breast cancer depend on accurate identification of women at increased risk. We present a risk prediction model that incorporates histologic features of biopsy tissues from women with benign breast disease (BBD) and compare its performance to the Breast Cancer Risk Assessment Tool (BCRAT). Methods We estimated the age-specific incidence of breast cancer and death from the Mayo BBD cohort and then combined these estimates with a relative risk model derived from 377 patient cases with breast cancer and 734 matched controls sampled from the Mayo BBD cohort to develop the BBD–to–breast cancer (BBD-BC) risk assessment tool. We validated the model using an independent set of 378 patient cases with breast cancer and 728 matched controls from the Mayo BBD cohort and compared the risk predictions from our model with those from the BCRAT. Results The BBD-BC model predicts the probability of breast cancer in women with BBD using tissue-based and other risk factors. The concordance statistic from the BBD-BC model was 0.665 in the model development series and 0.629 in the validation series; these values were higher than those from the BCRAT (0.567 and 0.472, respectively). The BCRAT significantly underpredicted breast cancer risk after benign biopsy (P = .004), whereas the BBD-BC predictions were appropriately calibrated to observed cancers (P = .247). Conclusion We developed a model using both demographic and histologic features to predict breast cancer risk in women with BBD. Our model more accurately classifies a woman's breast cancer risk after a benign biopsy than the BCRAT.
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Derouane, Françoise, Cédric van Marcke, Martine Berlière, Amandine Gerday, Latifa Fellah, Isabelle Leconte, Mieke R. Van Bockstal, Christine Galant, Cyril Corbet, and Francois P. Duhoux. "Predictive Biomarkers of Response to Neoadjuvant Chemotherapy in Breast Cancer: Current and Future Perspectives for Precision Medicine." Cancers 14, no. 16 (August 11, 2022): 3876. http://dx.doi.org/10.3390/cancers14163876.
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Pathological complete response (pCR) after neoadjuvant chemotherapy in patients with early breast cancer is correlated with better survival. Meanwhile, an expanding arsenal of post-neoadjuvant treatment strategies have proven beneficial in the absence of pCR, leading to an increased use of neoadjuvant systemic therapy in patients with early breast cancer and the search for predictive biomarkers of response. The better prediction of response to neoadjuvant chemotherapy could enable the escalation or de-escalation of neoadjuvant treatment strategies, with the ultimate goal of improving the clinical management of early breast cancer. Clinico-pathological prognostic factors are currently used to estimate the potential benefit of neoadjuvant systemic treatment but are not accurate enough to allow for personalized response prediction. Other factors have recently been proposed but are not yet implementable in daily clinical practice or remain of limited utility due to the intertumoral heterogeneity of breast cancer. In this review, we describe the current knowledge about predictive factors for response to neoadjuvant chemotherapy in breast cancer patients and highlight the future perspectives that could lead to the better prediction of response, focusing on the current biomarkers used for clinical decision making and the different gene signatures that have recently been proposed for patient stratification and the prediction of response to therapies. We also discuss the intratumoral phenotypic heterogeneity in breast cancers as well as the emerging techniques and relevant pre-clinical models that could integrate this biological factor currently limiting the reliable prediction of response to neoadjuvant systemic therapy.
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Ye, Guolin, Suqun He, Ruilin Pan, Lewei Zhu, Dan Zhou, and RuiLiang Lu. "Research on DCE-MRI Images Based on Deep Transfer Learning in Breast Cancer Adjuvant Curative Effect Prediction." Journal of Healthcare Engineering 2022 (February 23, 2022): 1–10. http://dx.doi.org/10.1155/2022/4477099.
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Breast cancer is a serious threat to women's physical and mental health. In recent years, its incidence has been on the rise and it has become the top female malignant tumor in China. At present, adjuvant chemotherapy for breast cancer has become the standard mode of breast cancer treatment, but the response results usually need to be completed after the implementation of adjuvant chemotherapy, and the optimization of the treatment plan and the implementation of breast-conserving therapy need to be based on accurate estimation of the pathological response. Therefore, to predict the efficacy of adjuvant chemotherapy for breast cancer patients is to find a predictive method that is conducive to individualized choice of chemotherapy regimens. This article introduces the research of DCE-MRI images based on deep transfer learning in breast cancer adjuvant curative effect prediction. Deep transfer learning algorithms are used to process images, and then, the features of breast cancer after adjuvant chemotherapy are collected through image feature collection. Predictions are made, and the research results show that the accuracy of the prediction reaches 70%.
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McCarthy, Anne Marie, Zoe Guan, Michaela Welch, Molly E. Griffin, Dorothy A. Sippo, Zhengyi Deng, Suzanne B. Coopey, et al. "Performance of Breast Cancer Risk-Assessment Models in a Large Mammography Cohort." JNCI: Journal of the National Cancer Institute 112, no. 5 (September 26, 2019): 489–97. http://dx.doi.org/10.1093/jnci/djz177.
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Abstract Background Several breast cancer risk-assessment models exist. Few studies have evaluated predictive accuracy of multiple models in large screening populations. Methods We evaluated the performance of the BRCAPRO, Gail, Claus, Breast Cancer Surveillance Consortium (BCSC), and Tyrer-Cuzick models in predicting risk of breast cancer over 6 years among 35 921 women aged 40–84 years who underwent mammography screening at Newton-Wellesley Hospital from 2007 to 2009. We assessed model discrimination using the area under the receiver operating characteristic curve (AUC) and assessed calibration by comparing the ratio of observed-to-expected (O/E) cases. We calculated the square root of the Brier score and positive and negative predictive values of each model. Results Our results confirmed the good calibration and comparable moderate discrimination of the BRCAPRO, Gail, Tyrer-Cuzick, and BCSC models. The Gail model had slightly better O/E ratio and AUC (O/E = 0.98, 95% confidence interval [CI] = 0.91 to 1.06, AUC = 0.64, 95% CI = 0.61 to 0.65) compared with BRCAPRO (O/E = 0.94, 95% CI = 0.88 to 1.02, AUC = 0.61, 95% CI = 0.59 to 0.63) and Tyrer-Cuzick (version 8, O/E = 0.84, 95% CI = 0.79 to 0.91, AUC = 0.62, 95% 0.60 to 0.64) in the full study population, and the BCSC model had the highest AUC among women with available breast density information (O/E = 0.97, 95% CI = 0.89 to 1.05, AUC = 0.64, 95% CI = 0.62 to 0.66). All models had poorer predictive accuracy for human epidermal growth factor receptor 2 positive and triple-negative breast cancers than hormone receptor positive human epidermal growth factor receptor 2 negative breast cancers. Conclusions In a large cohort of patients undergoing mammography screening, existing risk prediction models had similar, moderate predictive accuracy and good calibration overall. Models that incorporate additional genetic and nongenetic risk factors and estimate risk of tumor subtypes may further improve breast cancer risk prediction.
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Dutta, Shawni, Jyotsna Kumar Mandal, Tai Hoon Kim, and Samir Kumar Bandyopadhyay. "Breast Cancer Prediction Using Stacked GRU-LSTM-BRNN." Applied Computer Systems 25, no. 2 (December 1, 2020): 163–71. http://dx.doi.org/10.2478/acss-2020-0018.
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Abstract Breast Cancer diagnosis is one of the most studied problems in the medical domain. Cancer diagnosis has been studied extensively, which instantiates the need for early prediction of cancer disease. To obtain advance prediction, health records are exploited and given as input to an automated system. The paper focuses on constructing an automated system by employing deep learning based recurrent neural network models. A stacked GRU-LSTM-BRNN is proposed in this paper that accepts health records of a patient for determining the possibility of being affected by breast cancer. The proposed model is compared against other baseline classifiers such as stacked simple-RNN model, stacked LSTM-RNN model, stacked GRU-RNN model. Comparative results obtained in this study indicate that the stacked GRU-LSTM-BRNN model yields better classification performance for predictions related to breast cancer disease.
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Agana, Moses A., Chukwuemeka Odi Agwu, and Nsinem A. Ukpoho. "Breast Cancer Prediction and Control Using BiLSTM and Two-Dimensional Convolutional Neural Network." International Journal of Software Innovation 11, no. 1 (January 20, 2023): 1–19. http://dx.doi.org/10.4018/ijsi.316169.
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Breast cancer has a devastating effect on women. Different strategies of breast cancer classification exist with minimal work done on the prediction of the occurrence of the disease in potential carriers. In this study, a breast cancer predictive system has been developed using bidirectional long short-term memory (BiLSTM) for feature extraction and learning while the two-dimensional convolutional neural network (CNN) was used for breast cancer classification. Histopathological images were used for cancer prediction. Python was used as the programming language for implementing the system. The model was tested using datasets from The Cancer Imaging Archive (TCIA) repository. An accuracy level of 98.8% (higher than the most recent existing model) was achieved for the prediction of the future occurrence of breast cancer based on the tests on the dataset. The application of the model using live data from women can help in the prediction and control of the occurrence of breast cancer amongst women.
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L, Shakkeera, Rahul Raj Pandey, Rahul Bhardwaj, Sidhya Virya Singh, and Siddhartha S. Mukherjee. "Analysis and Prediction of Breast Cancer using Machine Learning Techniques." International Journal of Engineering and Advanced Technology 10, no. 2 (December 30, 2020): 26–30. http://dx.doi.org/10.35940/ijeat.b1968.1210220.
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Rapid multiplication of cells in the human body leads to cancer. It is the foremost cause of death due to cancer in females, after lung cancer. As the breast cancer is one of the recurrent kinds of cancer, diagnosis of breast cancer recurring is extremelyessential to increase the survival rate of patient suffering from it. Although cancer is avertible and also treatable in primary/early stages yet a vast number of patients are diagnosed with cancer when it is very late. Almost 8% of females are detected with breast cancer. Its characteristics are mutation of genes, constant pain, changes in the size and redness of skin texture of breasts. With the development of technology and machine learning techniques, cancer diagnosis and detection accuracy has greatly improved. This paper presents an outline of evolved machine learning techniques in this medical field by applying machine learning algorithms on breast cancer dataset like Logistic regression, Random Forest, Decision Trees (DT) etc.
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Buhl, Anna Sofie Kappel, Ib Jarle Christensen, Steen Knudsen, and Peter Buhl Jensen. "Doxorubicin response prediction in neoadjuvant breast cancer therapy." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e12119-e12119. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e12119.
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e12119 Background: Neoadjuvant treatment for breast cancer (BC) is an important remedy if the tumors are responding adequately. Still, there is no biomarker guidance resulting in many patients receiving chemotherapy with no efficient antitumor effect. We study a multigene mRNA-based technology for drug response prediction (DRP). DRP has been thoroughly validated in other settings, latest in prediction of epirubicin efficacy in advanced BC [1]. Here are the results of the DRP method in the prediction of response to neoadjuvant doxorubicin in early BC. Methods: The DRP correlates sensitivity of the applicable drug in cell lines with background mRNA. This is combined with gene expression patterns from >2.000 tumors of different origin to ensure clinically usefulness. The higher DRP score, the higher likelihood of response. Blinded predictions of doxorubicin DRP scores were compared to response data which originated from a phase II trial [2], where the study population received neoadjuvant doxorubicin and cyclophosphamide (N=279). The patients had histologically confirmed primary invasive breast adenocarcinoma (T2-3, N0-3, M0, tumor size ≥ 2.0 cm, ER+/- and HER2+/-). Statistical analysis was done using logistic regression. Results: Table shows the distribution of response and DRP scores, showing higher DRPs are correlated to better clinical outcome. This is demonstrated by logistic regression ( p=0.002), odds ratio for response 2.2 (95% CI:1.3-3.4). Multivariate analysis showed that the DRP was independent of other covariates. Conclusions: The DRP can predict which patients will be high likelihood responders to neoadjuvant doxorubicin. Modern multigene technologies may help assist clinicians in choosing between upfront surgery or neoadjuvant chemotherapy. Complete response (CR), Partial response (PR), Stable disease (SD), Progression of Disease (PD). 1. Buhl ASK, et al. (2018): Predicting efficacy of epirubicin by a multigene assay in advanced breast cancer within a Danish Breast Cancer Cooperative Group (DBCG) cohort: a retrospective-prospective blinded study. Breast cancer research and treatment. doi: 10.1007/s10549-018-4918-4. 2. Horak CE et al. (2013): Biomarker analysis of neoadjuvant doxorubicin/cyclophosphamide followed by ixabepilone or Paclitaxel in early-stage breast cancer. Clinical cancer research. doi: 10.1158/1078-0432.ccr-12-1359.[Table: see text]
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Ciatto, Stefano, Marco Rosselli Del Turco, Silvia Cecchini, Grazia Grazzini, and Anna Iossa. "Telethermography and Breast Cancer Risk Prediction." Tumori Journal 75, no. 2 (April 1989): 110–12. http://dx.doi.org/10.1177/030089168907500206.
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The authors report on 4,624 noncancer women classified by telethermography (TH1-2 vs TH3) and followed for an average of 6.6 years (range, 2–12). Breast cancer occurring beyond the sixth month from TH were recorded according to a Cancer Registry, and the association between breast cancer incidence and thermographic class or patient age was evaluated. Univariate analysis showed a significant association of age and thermography with further cancer incidence, but multivariate analysis (Cox's model) confirmed a significant association only for age. Thermography (TH3) showed a nonsignificant odds ratio of 1.6 with respect to TH1-2 cases. Thus thermography did not show any practical role as a breast cancer risk indicator. Possible biases affecting previous reports suggesting the use of thermography as a breast cancer risk indicator are discussed.
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Zain, Zuhaira Muhammad, Mona Alshenaifi, Abeer Aljaloud, Tamadhur Albednah, Reham Alghanim, Alanoud Alqifari, and Amal Alqahtani. "Predicting breast cancer recurrence using principal component analysis as feature extraction: an unbiased comparative analysis." International Journal of Advances in Intelligent Informatics 6, no. 3 (November 6, 2020): 313. http://dx.doi.org/10.26555/ijain.v6i3.462.
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Breast cancer recurrence is among the most noteworthy fears faced by women. Nevertheless, with modern innovations in data mining technology, early recurrence prediction can help relieve these fears. Although medical information is typically complicated, and simplifying searches to the most relevant input is challenging, new sophisticated data mining techniques promise accurate predictions from high-dimensional data. In this study, the performances of three established data mining algorithms: Naïve Bayes (NB), k-nearest neighbor (KNN), and fast decision tree (REPTree), adopting the feature extraction algorithm, principal component analysis (PCA), for predicting breast cancer recurrence were contrasted. The comparison was conducted between models built in the absence and presence of PCA. The results showed that KNN produced better prediction without PCA (F-measure = 72.1%), whereas the other two techniques: NB and REPTree, improved when used with PCA (F-measure = 76.1% and 72.8%, respectively). This study can benefit the healthcare industry in assisting physicians in predicting breast cancer recurrence precisely.
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Challa, Ramya. "Breast Cancer Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 3560–66. http://dx.doi.org/10.22214/ijraset.2022.44488.
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Abstract: Breast cancer is one ofi the diseases which cause number ofi deaths ever year across the globe, early detection and diagnosis ofi such type ofi disease is a challenging task ini orderi to reduce the numberi ofi deaths. Now a days various techniques ofi machinei learning and data miningi are used for medical diagnosis which has proven there metal by which prediction can be done for the chronic diseases like canceri which can save the life’s ofi the patients suffering from such type ofi disease. The major concern ofi this study is to findi the prediction accuracy ofi the classification algorithms like Support Vector Machine, J48, Naïve Bayes and Randomi Forest and to suggest the best algorithm.
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Banin Hirata, Bruna Karina, Julie Massayo Maeda Oda, Roberta Losi Guembarovski, Carolina Batista Ariza, Carlos Eduardo Coral de Oliveira, and Maria Angelica Ehara Watanabe. "Molecular Markers for Breast Cancer: Prediction on Tumor Behavior." Disease Markers 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/513158.
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Breast cancer is one of the most common cancers with greater than 1,300,000 cases and 450,000 deaths each year worldwide. The development of breast cancer involves a progression through intermediate stages until the invasive carcinoma and finally into metastatic disease. Given the variability in clinical progression, the identification of markers that could predict the tumor behavior is particularly important in breast cancer. The determination of tumor markers is a useful tool for clinical management in cancer patients, assisting in diagnostic, staging, evaluation of therapeutic response, detection of recurrence and metastasis, and development of new treatment modalities. In this context, this review aims to discuss the main tumor markers in breast carcinogenesis. The most well-established breast molecular markers with prognostic and/or therapeutic value like hormone receptors,HER-2oncogene, Ki-67, and p53 proteins, and the genes for hereditary breast cancer will be presented. Furthermore, this review shows the new molecular targets in breast cancer:CXCR4, caveolin, miRNA, andFOXP3, as promising candidates for future development of effective and targeted therapies, also with lower toxicity.
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Patel, Hardi, and Dr Mehul P. Barot. "Big Data Analytics to Predict Breast Cancer." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 2004–9. http://dx.doi.org/10.22214/ijraset.2022.41045.
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Abstract: Breast Cancer is the second cause of death among women. Early prediction of breast cancer will help with the survival of breast cancer patient. Machine Learning and Data Mining have been widely used in the prediction of breast cancer and on the early detection of breast cancer. This paper compares the machine learning techniques which are used for the prediction of breast cancer. Keywords: Breast Cancer, Malignant, Benign, Machine Learning, Big Data Analytics.
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A. Abassi, Rahibu, Amina S. Msengwa, and Rocky R. J. Akarro. "Imputation methods on retrospective breast cancer data in Tanzania: A comparative study." Women Health Care and Issues 5, no. 4 (June 6, 2022): 01–09. http://dx.doi.org/10.31579/2642-9756/118.
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Background: Clinical datasets are at risk of having missing data for several reasons including patients’ failure to attend clinical measurements and measurement recorder’s defects. Missing data can significantly affect the analysis and results might be doubtful due to bias caused by omission incomplete records during analysis especially if a dataset is small. This study aims to compare several imputation methods in terms of efficiency in filling-in missing data so as to increase prediction and classification accuracy in breast cancer dataset. Methodology: Five imputation methods namely series mean, k-nearest neighbour, hot deck, predictive mean matching, expected maximisation via bootstrapping, and multiple imputation by chained equations were applied to replace the missing values to the real breast cancer dataset. The efficiency of imputation methods was compared by using the Root Mean Square Errors and Mean Absolute Errors to obtain a suitable complete dataset. Binary logistic regression and linear discrimination classifiers were applied to the imputed dataset to compare their efficacy on classification and discrimination. Results: The evaluation of imputation methods revealed that the predictive mean matching method was better off compared to other imputation methods. In addition, the binary logistic regression and linear discriminant analyses yield almost similar values on overall classification rates, sensitivity and specificity. Conclusion: The predictive mean matching imputation showed higher accuracy in estimating and replacing missing data values in a real breast cancer dataset under the study. It is a more effective and good approach to handle missing data. We recommend replacing missing data by using predictive mean matching since it is a plausible approach toward multiple imputations for numerical variables. It improves estimation and prediction accuracy over the use complete-case analysis especially when percentage of missing data is not very small.
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Mrs N. Vanitha, R. Srimathi, and J Haritha. "Analysis of Machine Learning Techniques for Breast Cancer Prediction." International Journal of Engineering and Management Research 11, no. 1 (February 9, 2021): 79–83. http://dx.doi.org/10.31033/ijemr.11.1.12.
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The most frequently happening cancer among Indian women is breast cancer, which is the second most exposed cancer in the world. Here is a chance of fifty percent for fatality in a case as one of two women diagnosed with breast cancer die in the cases of Indian women. With the rapid population growth, the risk of death incurred by breast cancer is rising exponentially. [2] Breast cancer is the second most severe cancer among all of the cancers already unveiled. A machine learning technique discovers illness which helps clinical staffs in sickness analysis and offers dependable, powerful, and quick reaction just as diminishes the danger of death. In this paper, we look at five administered AI methods named Support vector machine (SVM), K-closest neighbours, irregular woodlands, fake/ Artificial neural organizations (ANNs). The performance of the study is measured with respect to accuracy, sensitivity, specificity, precision, negative predictive value. Furthermore, these strategies were evaluated on exactness review region under bend and beneficiary working trademark bend. At last in this paper we analysed some of different papers to find how they are predicted and what are all the techniques they were used and finally we study the complete research of machine learning techniques for breast cancer.
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Chuang, Li-Yeh, Guang-Yu Chen, Sin-Hua Moi, Fu Ou-Yang, Ming-Feng Hou, and Cheng-Hong Yang. "Relationship between Clinicopathologic Variables in Breast Cancer Overall Survival Using Biogeography-Based Optimization Algorithm." BioMed Research International 2019 (April 1, 2019): 1–12. http://dx.doi.org/10.1155/2019/2304128.
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Breast cancer is the most common cancer among women and is considered a major public health concern worldwide. Biogeography-based optimization (BBO) is a novel metaheuristic algorithm. This study analyzed the relationship between the clinicopathologic variables of breast cancer using Cox proportional hazard (PH) regression on the basis of the BBO algorithm. The dataset is prospectively maintained by the Division of Breast Surgery at Kaohsiung Medical University Hospital. A total of 1896 patients with breast cancer were included and tracked from 2005 to 2017. Fifteen general breast cancer clinicopathologic variables were collected. We used the BBO algorithm to select the clinicopathologic variables that could potentially contribute to predicting breast cancer prognosis. Subsequently, Cox PH regression analysis was used to demonstrate the association between overall survival and the selected clinicopathologic variables. C-statistics were used to test predictive accuracy and the concordance of various survival models. The BBO-selected clinicopathologic variables model obtained the highest C-statistic value (80%) for predicting the overall survival of patients with breast cancer. The selected clinicopathologic variables included tumor size (hazard ratio [HR] 2.372, p = 0.006), lymph node metastasis (HR 1.301, p = 0.038), lymphovascular invasion (HR 1.606, p = 0.096), perineural invasion (HR 1.546, p = 0.168), dermal invasion (HR 1.548, p = 0.028), total mastectomy (HR 1.633, p = 0.092), without hormone therapy (HR 2.178, p = 0.003), and without chemotherapy (HR 1.234, p = 0.491). This number was the minimum number of discriminators required for optimal discrimination in the breast cancer overall survival model with acceptable prediction ability. Therefore, on the basis of the clinicopathologic variables, the survival prediction model in this study could contribute to breast cancer follow-up and management.
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reddy, Anuradha. "Support Vector Machine Classifier For Prediction Of Breast Malignancy Using Wisconsin Breast Cancer Dataset." Journal of Artificial Intelligence, Machine Learning and Neural Network, no. 21 (January 1, 2022): 1–8. http://dx.doi.org/10.55529/jaimlnn.21.1.8.
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Cancer is the world's second largest cause of death. In 2018, 9.6 million people died from cancer. In any medical sickness, breast cancer is one of the most delicate and endemic diseases. This is one of the primary causes of female death in the world. Breast cancer kills one out of every eleven women around the world. "Early detection equals improved odds of survival," says a well-known cancer adage. As a result, early detection is essential for successfully preventing breast cancer and lowering morality. Breast Cancer is a type of cancer that affects one of the most significant issues that humanity has faced in recent decades has been diagnosis and prediction. Cancer detection that is accurate can save millions of lives. Effective technologies for diagnosing malignant breasts aid healthcare providers in diagnosing and treating patients in a fast and accurate manner. Experiments were carried out in this study to categorise breast cancer as benign or malignant using the Wisconsin Diagnosis Breast Cancer (WDBC) database. Support Vector Machine is a supervised learning technique (SVM). The SVM classifier's classification performance is evaluated. Experiments demonstrate that the SVM model has a fantastic performance, with a classification accuracy of 96.09 percent on the testing subset.
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reddy, Anuradha. "Support Vector Machine Classifier For Prediction Of Breast Malignancy Using Wisconsin Breast Cancer Dataset." Journal of Artificial Intelligence, Machine Learning and Neural Network, no. 21 (January 1, 2022): 1–8. http://dx.doi.org/10.55529/jaimlnn21.1.8.
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Cancer is the world's second largest cause of death. In 2018, 9.6 million people died from cancer. In any medical sickness, breast cancer is one of the most delicate and endemic diseases. This is one of the primary causes of female death in the world. Breast cancer kills one out of every eleven women around the world. "Early detection equals improved odds of survival," says a well-known cancer adage. As a result, early detection is essential for successfully preventing breast cancer and lowering morality. Breast Cancer is a type of cancer that affects one of the most significant issues that humanity has faced in recent decades has been diagnosis and prediction. Cancer detection that is accurate can save millions of lives. Effective technologies for diagnosing malignant breasts aid healthcare providers in diagnosing and treating patients in a fast and accurate manner. Experiments were carried out in this study to categorise breast cancer as benign or malignant using the Wisconsin Diagnosis Breast Cancer (WDBC) database. Support Vector Machine is a supervised learning technique (SVM). The SVM classifier's classification performance is evaluated. Experiments demonstrate that the SVM model has a fantastic performance, with a classification accuracy of 96.09 percent on the testing subset.
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Häberle, Lothar, Alexander Hein, Matthias Rübner, Michael Schneider, Arif Ekici, Paul Gass, Arndt Hartmann, et al. "Predicting Triple-Negative Breast Cancer Subtype Using Multiple Single Nucleotide Polymorphisms for Breast Cancer Risk and Several Variable Selection Methods." Geburtshilfe und Frauenheilkunde 77, no. 06 (June 2017): 667–78. http://dx.doi.org/10.1055/s-0043-111602.
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Abstract Introduction Studies of triple-negative breast cancer have recently been extending the inclusion criteria and incorporating additional molecular markers into the selection criteria, opening up scope for targeted therapies. The screening phases required for studies of this type are often prolonged, since the process of determining the molecular subtype and carrying out additional biomarker assessment is time-consuming. Parameters such as germline genotypes capable of predicting the molecular subtype before it becomes available from pathology might be helpful for treatment planning and optimizing the timing and cost of screening phases. This appears to be feasible, as rapid and low-cost genotyping methods are becoming increasingly available. The aim of this study was to identify single nucleotide polymorphisms (SNPs) for breast cancer risk capable of predicting triple negativity, in addition to clinical predictors, in breast cancer patients. Methods This cross-sectional observational study included 1271 women with invasive breast cancer who were treated at a university hospital. A total of 76 validated breast cancer risk SNPs were successfully genotyped. Univariate associations between each SNP and triple negativity were explored using logistic regression analyses. Several variable selection and regression techniques were applied to identify a set of SNPs that together improve the prediction of triple negativity in addition to the clinical predictors of age at diagnosis and body mass index (BMI). The most accurate prediction method was determined by cross-validation. Results The SNP rs10069690 (TERT, CLPTM1L) was the only significant SNP (corrected p = 0.02) after correction of p values for multiple testing in the univariate analyses. This SNP and three additional SNPs from the genes RAD51B, CCND1, and FGFR2 were selected for prediction of triple negativity. The addition of these SNPs to clinical predictors increased the cross-validated area under the curve (AUC) from 0.618 to 0.625. Age at diagnosis was the strongest predictor, stronger than any genetic characteristics. Conclusion Prediction of triple-negative breast cancer can be improved if SNPs associated with breast cancer risk are added to a prediction rule based on age at diagnosis and BMI. This finding could be used for prescreening purposes in complex molecular therapy studies for triple-negative breast cancer.
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Jain, Somil, and Puneet Kumar. "Prediction of Breast Cancer Using Machine Learning." Recent Advances in Computer Science and Communications 13, no. 5 (November 5, 2020): 901–8. http://dx.doi.org/10.2174/2213275912666190617160834.
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Background:: Breast cancer is one of the diseases which cause number of deaths ever year across the globe, early detection and diagnosis of such type of disease is a challenging task in order to reduce the number of deaths. Now a days various techniques of machine learning and data mining are used for medical diagnosis which has proven there metal by which prediction can be done for the chronic diseases like cancer which can save the life’s of the patients suffering from such type of disease. The major concern of this study is to find the prediction accuracy of the classification algorithms like Support Vector Machine, J48, Naïve Bayes and Random Forest and to suggest the best algorithm. Objective:: The objective of this study is to assess the prediction accuracy of the classification algorithms in terms of efficiency and effectiveness. Methods: This paper provides a detailed analysis of the classification algorithms like Support Vector Machine, J48, Naïve Bayes and Random Forest in terms of their prediction accuracy by applying 10 fold cross validation technique on the Wisconsin Diagnostic Breast Cancer dataset using WEKA open source tool. Results:: The result of this study states that Support Vector Machine has achieved the highest prediction accuracy of 97.89 % with low error rate of 0.14%. Conclusion:: This paper provides a clear view over the performance of the classification algorithms in terms of their predicting ability which provides a helping hand to the medical practitioners to diagnose the chronic disease like breast cancer effectively.
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Jojan, Janjira, and Anongnart Srivihok. "Duo Bundling Algorithms for Data Preprocessing: Case Study of Breast Cancer Data Prediction." Lecture Notes on Software Engineering 2, no. 4 (2014): 375–79. http://dx.doi.org/10.7763/lnse.2014.v2.153.
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Li, Huayao, Chundi Gao, Jing Zhuang, Lijuan Liu, Jing Yang, Cun Liu, Chao Zhou, Fubin Feng, Ruijuan Liu, and Changgang Sun. "An mRNA characterization model predicting survival in patients with invasive breast cancer based on The Cancer Genome Atlas database." Cancer Biomarkers 30, no. 4 (April 9, 2021): 417–28. http://dx.doi.org/10.3233/cbm-201684.
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BACKGROUND: Invasive breast cancer is a highly heterogeneous tumor, although there have been many prediction methods for invasive breast cancer risk prediction, the prediction effect is not satisfactory. There is an urgent need to develop a more accurate method to predict the prognosis of patients with invasive breast cancer. OBJECTIVE: To identify potential mRNAs and construct risk prediction models for invasive breast cancer based on bioinformatics METHODS: In this study, we investigated the differences in mRNA expression profiles between invasive breast cancer and normal breast samples, and constructed a risk model for the prediction of prognosis of invasive breast cancer with univariate and multivariate Cox analyses. RESULTS: We constructed a risk model comprising 8 mRNAs (PAX7, ZIC2, APOA5, TP53AIP1,MYBPH, USP41, DACT2, and POU3F2) for the prediction of invasive breast cancer prognosis. We used the 8-mRNA risk prediction model to divide 1076 samples into high-risk groups and low-risk groups, the Kaplan-Meier curve showed that the high-risk group was closely related to the poor prognosis of overall survival in patients with invasive breast cancer. The receiver operating characteristic curve revealed an area under the curve of 0.773 for the 8 mRNA model at 3-year overall survival, indicating that this model showed good specificity and sensitivity for prediction of prognosis of invasive breast cancer. CONCLUSIONS: The study provides an effective bioinformatic analysis for the better understanding of the molecular pathogenesis and prognosis risk assessment of invasive breast cancer.
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Zhang, Fan, Youping Deng, and Renee Drabier. "Multiple Biomarker Panels for Early Detection of Breast Cancer in Peripheral Blood." BioMed Research International 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/781618.
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Detecting breast cancer at early stages can be challenging. Traditional mammography and tissue microarray that have been studied for early breast cancer detection and prediction have many drawbacks. Therefore, there is a need for more reliable diagnostic tools for early detection of breast cancer due to a number of factors and challenges. In the paper, we presented a five-marker panel approach based on SVM for early detection of breast cancer in peripheral blood and show how to use SVM to model the classification and prediction problem of early detection of breast cancer in peripheral blood. We found that the five-marker panel can improve the prediction performance (area under curve) in the testing data set from 0.5826 to 0.7879. Further pathway analysis showed that the top four five-marker panels are associated with signaling, steroid hormones, metabolism, immune system, and hemostasis, which are consistent with previous findings. Our prediction model can serve as a general model for multibiomarker panel discovery in early detection of other cancers.
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Gousbi, B., and A. R. Mohamed Shanavas. "A Study: Breast Cancer Prediction Using Data Mining Techniques." Asian Journal of Computer Science and Technology 8, S2 (March 5, 2019): 52–56. http://dx.doi.org/10.51983/ajcst-2019.8.s2.2025.
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Data mining is the extraction of unseen predictive info from huge databases, is the process of arranging through enormous data sets to recognize patterns and create relationships to resolve the problems through data analysis. Cancer is one of the primary reasons of death wide-reaching. Timely detection and prevention of cancer plays a very vital role in decreasing deaths affected by cancer. Identification of genetic and environmental factors is very significant in emerging novel methods to identify and avert cancer. Many researchers’ use data mining techniques like clustering, classification and prediction find potential cancer patients. This paper focuses on a breast cancer prediction system built on data mining techniques. With the help of this system, people can guess the possibility of the breast cancer in the former stage itself.
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Chlebowski, R. T., G. L. Anderson, D. S. Lane, A. Aragaki, T. Rohan, S. Yasmeen, G. Sato, C. A. Rosenberg, and F. A. Hubbell. "Predicting risk of estrogen receptor positive breast cancers in postmenopausal women." Journal of Clinical Oncology 25, no. 18_suppl (June 20, 2007): 1507. http://dx.doi.org/10.1200/jco.2007.25.18_suppl.1507.
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1507 Background: Chemoprevention strategies for estrogen receptor positive (ER+) breast cancers are emerging, especially for postmenopausal women, but require methods of targeting appropriate populations. Our objective was to improve the Breast Cancer Risk Assessment Tool [Gail Model (GM)] for estimating ER+ breast cancer risk. Methods: A prospective cohort involving 161,809 postmenopausal women aged 50–79 years, (93,676 in the observational study (OS) and 68,132 in clinical trials (CT)) at Women’s Health Initiative (WHI) Clinical Centers had comprehensive assessment of lifestyle, medication use and breast cancer risk factors. Breast cancer risk from the GM and other models incorporating additional or fewer risk factors and five year incidence of ER + and ER negative (ER-) invasive breast cancers were determined. Main outcome measures were concordance statistics for models predicting breast cancer risk. Results: Of 148,266 women meeting eligibility criteria, (no prior breast cancer and/or mastectomy), 3,236 developed breast cancer. Chronological age and age at menopause, both GM components, were significantly associated with only ER+ but not ER- breast cancer risk (p<0.05 for heterogeneity test). The GM predicted population-based ER+ cancer risk with reasonable accuracy (concordance statistic 0.60, 95% confidence interval (CI) 0.58 to 0.62) but for ER- cancers, the results were equivalent to chance allocation (concordance statistic 0.49, 95% CI 0.45 to 0.54). For ER+ cancers, no additional risk factors improved the GM prediction. However, a simpler model, developed in the OS and tested in the CT population, including only age, family history, and benign breast biopsy was comparable to GM in ER+ breast cancer prediction (concordance statistics 0.58, 95% CI 0.56 to 0.60). Using this model, all women ≥ 55 years old (or ≥ 60 year old if African American) with either a prior breast biopsy or first degree breast cancer family history had five year breast cancer risk of ≥ 1.8%. Conclusions: In postmenopausal women with comprehensive mammography use, the GM identifies populations at increased risk for ER+ breast cancer but not for ER- cancer. A model with fewer variables provides a simpler alternative for identifying populations appropriate for breast cancer chemoprevention interventions. No significant financial relationships to disclose.
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Nasir, Muhammad Umar, Taher M. Ghazal, Muhammad Adnan Khan, Muhammad Zubair, Atta-ur Rahman, Rashad Ahmed, Hussam Al Hamadi, and Chan Yeob Yeun. "Breast Cancer Prediction Empowered with Fine-Tuning." Computational Intelligence and Neuroscience 2022 (June 9, 2022): 1–9. http://dx.doi.org/10.1155/2022/5918686.
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In the world, in the past recent five years, breast cancer is diagnosed about 7.8 million women’s and making it the most widespread cancer, and it is the second major reason for women’s death. So, early prevention and diagnosis systems of breast cancer could be more helpful and significant. Neural networks can extract multiple features automatically and perform predictions on breast cancer. There is a need for several labeled images to train neural networks which is a nonconventional method for some types of data images such as breast magnetic resonance imaging (MRI) images. So, there is only one significant solution for this query is to apply fine-tuning in the neural network. In this paper, we proposed a fine-tuning model using AlexNet in the neural network to extract features from breast cancer images for training purposes. So, in the proposed model, we updated the first and last three layers of AlexNet to detect the normal and abnormal regions of breast cancer. The proposed model is more efficient and significant because, during the training and testing process, the proposed model achieves higher accuracy 98.44% and 98.1% of training and testing, respectively. So, this study shows that the use of fine-tuning in the neural network can detect breast cancer using MRI images and train a neural network classifier by feature extraction using the proposed model is faster and more efficient.
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Syleouni, Maria Eleni, Nena Karavasiloglou, Laura Manduchi, Miriam Wanner, Dimitri Korol, and Sabine Rohrmann. "Abstract 2252: Predicting second breast cancers among women diagnosed with primary breast cancer using patient-level data and machine learning algorithms." Cancer Research 82, no. 12_Supplement (June 15, 2022): 2252. http://dx.doi.org/10.1158/1538-7445.am2022-2252.
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Abstract In 2020 2.3 million women were diagnosed with breast cancer. About 7.4% of women who have been diagnosed with primary breast cancer will have a second primary breast cancer within 10 years. This study builds a prediction model for second breast cancer for women who have had primary breast cancer. Readily available cancer registry data with machine learning methods for classification are employed. The best-performing model is selected based on the area under the receiver operator curve, and the key characteristics contributing to a high risk for second breast cancer are identified based on the prediction model. Using extreme gradient boosting (XGBoost) with limited patient features we find an area under the curve of 0.65-0.70 for the testing set. Among the most important features are days from incidence to treatment, size of primary tumor based on the pathology report, and oestrogen receptor status.This research is a step towards the development of a tool that will help doctors identify women very likely to develop second breast cancer, which will prioritize their follow-up or inform their course of treatment depending on their characteristics. Citation Format: Maria Eleni Syleouni, Nena Karavasiloglou, Laura Manduchi, Miriam Wanner, Dimitri Korol, Sabine Rohrmann. Predicting second breast cancers among women diagnosed with primary breast cancer using patient-level data and machine learning algorithms [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2252.
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Lo, Adeline, Herman Chernoff, Tian Zheng, and Shaw-Hwa Lo. "Why significant variables aren’t automatically good predictors." Proceedings of the National Academy of Sciences 112, no. 45 (October 26, 2015): 13892–97. http://dx.doi.org/10.1073/pnas.1518285112.
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Thus far, genome-wide association studies (GWAS) have been disappointing in the inability of investigators to use the results of identified, statistically significant variants in complex diseases to make predictions useful for personalized medicine. Why are significant variables not leading to good prediction of outcomes? We point out that this problem is prevalent in simple as well as complex data, in the sciences as well as the social sciences. We offer a brief explanation and some statistical insights on why higher significance cannot automatically imply stronger predictivity and illustrate through simulations and a real breast cancer example. We also demonstrate that highly predictive variables do not necessarily appear as highly significant, thus evading the researcher using significance-based methods. We point out that what makes variables good for prediction versus significance depends on different properties of the underlying distributions. If prediction is the goal, we must lay aside significance as the only selection standard. We suggest that progress in prediction requires efforts toward a new research agenda of searching for a novel criterion to retrieve highly predictive variables rather than highly significant variables. We offer an alternative approach that was not designed for significance, the partition retention method, which was very effective predicting on a long-studied breast cancer data set, by reducing the classification error rate from 30% to 8%.
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Yoon, Seokhyun, Hye Sung Won, Keunsoo Kang, Kexin Qiu, Woong June Park, and Yoon Ho Ko. "Hormone Receptor-Status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape." Cancers 12, no. 5 (May 5, 2020): 1165. http://dx.doi.org/10.3390/cancers12051165.
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The cost of next-generation sequencing technologies is rapidly declining, making RNA-seq-based gene expression profiling (GEP) an affordable technique for predicting receptor expression status and intrinsic subtypes in breast cancer patients. Based on the expression levels of co-expressed genes, GEP-based receptor-status prediction can classify clinical subtypes more accurately than can immunohistochemistry (IHC). Using data from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA BRCA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets, we identified common predictor genes found in both datasets and performed receptor-status prediction based on these genes. By assessing the survival outcomes of patients classified using GEP- or IHC-based receptor status, we compared the prognostic value of the two methods. We found that GEP-based HR prediction provided higher concordance with the intrinsic subtypes and a stronger association with treatment outcomes than did IHC-based hormone receptor (HR) status. GEP-based prediction improved the identification of patients who could benefit from hormone therapy, even in patients with non-luminal breast cancer. We also confirmed that non-matching subgroup classification affected the survival of breast cancer patients and that this could be largely overcome by GEP-based receptor-status prediction. In conclusion, GEP-based prediction provides more reliable classification of HR status, improving therapeutic decision making for breast cancer patients.
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Mahmoud, Mattia A., Anne Marie McCarthy, Despina Kontos, Emily Conant, Jinbo Chen, Sarah Ehsan, Lauren Pantalone, and Walter Mankowski. "Abstract P022: Quantitative measures of breast density and breast cancer risk prediction among black women in a screening population." Cancer Prevention Research 16, no. 1_Supplement (January 1, 2023): P022. http://dx.doi.org/10.1158/1940-6215.precprev22-p022.
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Abstract Background: Although mammographic density (MD) is a strong predictor of invasive breast cancer, it has been shown to increase the discriminatory ability of existing risk prediction models only slightly. Breast density is assessed visually by radiologists according to the Breast Imaging Reporting and Data System (BI-RADS) criteria, which has been shown to lack reproducibility. Additionally, the racial diversity of women included in the previous studies was limited. Quantitative measures of breast density have been developed that automatically measure density directly from images. Our prior work found that Black women had lower BI-RADS breast density, despite having a greater quantity of dense breast tissue on average compared with white women when quantitative measures were used. The study purpose was to determine if adding quantitative breast density measures improved breast cancer risk prediction for both white and Black women compared to the Breast Cancer Risk Assessment Tool (BCRAT). Methods: A total of 16,942 women (N=6881 white, N=10061 Black) screened with full-field digital mammography (FFDM) or with a combination of FFDM and digital breast tomosynthesis (DBT) at the Hospital of the University of Pennsylvania (HUP) between September 1, 2010 to December 31, 2014 were included. Area breast density measurements including dense area and area percent density were obtained using a fully automated, validated LIBRA software. All patients were followed from the date of first screening mammogram visit until breast cancer diagnosis or end of follow up on December 31, 2019. We used the BCRA R package (v2.1) for the BCRAT (https://dceg.cancer.gov/tools/risk-assessment/bcra) to estimate the expected 5 year absolute risk for breast cancer. We evaluated the area under the curve (AUC) and calibration (observed to expected ratio, O/E) of the following models: BCRAT alone, BCRAT + BI-RADS density, BCRAT + quantitative density measures, and BCRAT + BI-RADS density + quantitative density measures. Results: There were 123 breast cancers among white and 123 breast cancers among Black women. Adding dense area and area percent density to the BCRAT alone or BCRAT plus BI-RADS density did not improve predictive accuracy for white or Black women. AUC remained close to 0.59 for white women and 0.61 for Black women in all models, with no statistically significant differences in AUCs (DeLong Test p value = 0.09). Underprediction was worse in white women than in Black women. Under-prediction of the BCRAT was reduced when adding percent density from [O/E 1.24 vs. O/E 1.17] in white women. Calibration stayed relatively the same (O/E=1.10) for Black women even when adding both quantitative MD measures. Conclusion: Our results suggest that adding quantitative area mammographic density measurements to the BCRAT does not improves breast cancer risk prediction among Black or white women. Given the increasing use of digital breast tomosynthesis (DBT), future studies should examine whether volumetric breast density measures have superior predictive value among Black women. Citation Format: Mattia A. Mahmoud, Anne Marie McCarthy, Despina Kontos, Emily Conant, Jinbo Chen, Sarah Ehsan, Lauren Pantalone, Walter Mankowski. Quantitative measures of breast density and breast cancer risk prediction among black women in a screening population. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P022.
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Ma, Zhuo, Sijia Huang, Xiaoqing Wu, Yinying Huang, Sally Wai-Chi Chan, Yilan Lin, Xujuan Zheng, and Jiemin Zhu. "Development of a Prognostic App (iCanPredict) to Predict Survival for Chinese Women With Breast Cancer: Retrospective Study." Journal of Medical Internet Research 24, no. 3 (March 9, 2022): e35768. http://dx.doi.org/10.2196/35768.
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Background Accurate prediction of survival is crucial for both physicians and women with breast cancer to enable clinical decision making on appropriate treatments. The currently available survival prediction tools were developed based on demographic and clinical data obtained from specific populations and may underestimate or overestimate the survival of women with breast cancer in China. Objective This study aims to develop and validate a prognostic app to predict the overall survival of women with breast cancer in China. Methods Nine-year (January 2009-December 2017) clinical data of women with breast cancer who received surgery and adjuvant therapy from 2 hospitals in Xiamen were collected and matched against the death data from the Xiamen Center of Disease Control and Prevention. All samples were randomly divided (7:3 ratio) into a training set for model construction and a test set for model external validation. Multivariable Cox regression analysis was used to construct a survival prediction model. The model performance was evaluated by receiver operating characteristic (ROC) curve and Brier score. Finally, by running the survival prediction model in the app background thread, the prognostic app, called iCanPredict, was developed for women with breast cancer in China. Results A total of 1592 samples were included for data analysis. The training set comprised 1114 individuals and the test set comprised 478 individuals. Age at diagnosis, clinical stage, molecular classification, operative type, axillary lymph node dissection, chemotherapy, and endocrine therapy were incorporated into the model, where age at diagnosis (hazard ratio [HR] 1.031, 95% CI 1.011-1.051; P=.002), clinical stage (HR 3.044, 95% CI 2.347-3.928; P<.001), and endocrine therapy (HR 0.592, 95% CI 0.384-0.914; P=.02) significantly influenced the survival of women with breast cancer. The operative type (P=.81) and the other 4 variables (molecular classification [P=.91], breast reconstruction [P=.36], axillary lymph node dissection [P=.32], and chemotherapy [P=.84]) were not significant. The ROC curve of the training set showed that the model exhibited good discrimination for predicting 1- (area under the curve [AUC] 0.802, 95% CI 0.713-0.892), 5- (AUC 0.813, 95% CI 0.760-0.865), and 10-year (AUC 0.740, 95% CI 0.672-0.808) overall survival. The Brier scores at 1, 5, and 10 years after diagnosis were 0.005, 0.055, and 0.103 in the training set, respectively, and were less than 0.25, indicating good predictive ability. The test set externally validated model discrimination and calibration. In the iCanPredict app, when physicians or women input women’s clinical information and their choice of surgery and adjuvant therapy, the corresponding 10-year survival prediction will be presented. Conclusions This survival prediction model provided good model discrimination and calibration. iCanPredict is the first tool of its kind in China to provide survival predictions to women with breast cancer. iCanPredict will increase women’s awareness of the similar survival rate of different surgeries and the importance of adherence to endocrine therapy, ultimately helping women to make informed decisions regarding treatment for breast cancer.
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Liu, Jenny, Peh Joo Ho, Tricia Hui Ling Tan, Yen Shing Yeoh, Ying Jia Chew, Nur Khaliesah Mohamed Riza, Alexis Jiaying Khng, et al. "BREAst screening Tailored for HEr (BREATHE)—A study protocol on personalised risk-based breast cancer screening programme." PLOS ONE 17, no. 3 (March 31, 2022): e0265965. http://dx.doi.org/10.1371/journal.pone.0265965.
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Routine mammography screening is currently the standard tool for finding cancers at an early stage, when treatment is most successful. Current breast screening programmes are one-size-fits-all which all women above a certain age threshold are encouraged to participate. However, breast cancer risk varies by individual. The BREAst screening Tailored for HEr (BREATHE) study aims to assess acceptability of a comprehensive risk-based personalised breast screening in Singapore. Advancing beyond the current age-based screening paradigm, BREATHE integrates both genetic and non-genetic breast cancer risk prediction tools to personalise screening recommendations. BREATHE is a cohort study targeting to recruit ~3,500 women. The first recruitment visit will include questionnaires and a buccal cheek swab. After receiving a tailored breast cancer risk report, participants will attend an in-person risk review, followed by a final session assessing the acceptability of our risk stratification programme. Risk prediction is based on: a) Gail model (non-genetic), b) mammographic density and recall, c) BOADICEA predictions (breast cancer predisposition genes), and d) breast cancer polygenic risk score. For national implementation of personalised risk-based breast screening, exploration of the acceptability within the target populace is critical, in addition to validated predication tools. To our knowledge, this is the first study to implement a comprehensive risk-based mammography screening programme in Asia. The BREATHE study will provide essential data for policy implementation which will transform the health system to deliver a better health and healthcare outcomes.
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Alfian, Ganjar, Muhammad Syafrudin, Imam Fahrurrozi, Norma Latif Fitriyani, Fransiskus Tatas Dwi Atmaji, Tri Widodo, Nurul Bahiyah, Filip Benes, and Jongtae Rhee. "Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method." Computers 11, no. 9 (September 12, 2022): 136. http://dx.doi.org/10.3390/computers11090136.
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Developing a prediction model from risk factors can provide an efficient method to recognize breast cancer. Machine learning (ML) algorithms have been applied to increase the efficiency of diagnosis at the early stage. This paper studies a support vector machine (SVM) combined with an extremely randomized trees classifier (extra-trees) to provide a diagnosis of breast cancer at the early stage based on risk factors. The extra-trees classifier was used to remove irrelevant features, while SVM was utilized to diagnose the breast cancer status. A breast cancer dataset consisting of 116 subjects was utilized by machine learning models to predict breast cancer, while the stratified 10-fold cross-validation was employed for the model evaluation. Our proposed combined SVM and extra-trees model reached the highest accuracy up to 80.23%, which was significantly better than the other ML model. The experimental results demonstrated that by applying extra-trees-based feature selection, the average ML prediction accuracy was improved by up to 7.29% as contrasted to ML without the feature selection method. Our proposed model is expected to increase the efficiency of breast cancer diagnosis based on risk factors. In addition, we presented the proposed prediction model that could be employed for web-based breast cancer prediction. The proposed model is expected to improve diagnostic decision-support systems by predicting breast cancer disease accurately.
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Jeffrey, Stefanie S., Per Eystein Lønning, and Bruce E. Hillner. "Genomics-Based Prognosis and Therapeutic Prediction in Breast Cancer." Journal of the National Comprehensive Cancer Network 3, no. 3 (May 2005): 291–300. http://dx.doi.org/10.6004/jnccn.2005.0016.
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Breast cancer is a heterogeneous disease. DNA microarray technology is being applied to breast cancer to identify new prognostic biomarkers, to predict response to therapy, and to discover targets for the development of novel therapies. New diagnostic assays based on global gene expression are being introduced into clinical practice or tested in large-scale clinical trials. This review focuses on translational studies using microarray analyses and discusses best practice features and pitfalls. We note that factors that predict metastatic disease are not necessarily the same factors that predict therapeutic response. We believe that the characterization and discernment of different systems among breast cancers is crucial for understanding drug sensitivity and resistance mechanisms and for guiding therapy.
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Chairat, Rungnapa, Adisorn Puttisri, Asani Pamarapa, Sahatham Samintharapanya, Chamaiporn Tawichasri, and Jayanton Patumanond. "Are Both Ultrasonography and Mammography Necessary for Cancer Investigation of Breast Lumps in Resource-Limited Countries?" ISRN Oncology 2013 (August 28, 2013): 1–6. http://dx.doi.org/10.1155/2013/257942.
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Objective. To reevaluate the diagnostic value of breast imaging in the diagnosis of breast cancer in areas where health resources are limited. Methods. Patients were women presenting with breast lumps in two university-affiliated tertiary hospitals, Thailand, during 2006 and 2010. Clinical data were abstracted from the breast cancer registration database and patient records. The diagnostic predictive ability of ultrasonography and mammography was obtained from logistic regression analysis and presented with areas under the receiver operating characteristics (AuROCs) curves. Results. Among 3129 breast lumps (3069 women), 854 were diagnosed with breast cancer by certified pathologists. Age and size of lumps alone already predicted cancer correctly in 77.45% (AuROC = 77.45). Additional ultrasonography increased the prediction to 96.22% (P<0.001). Additional mammography also increased the prediction to 95.99% (P<0.001). Performing both imaging modalities did not increase the prediction clinically (0.01%–0.24%). More accurate prediction (2.07%–2.21%) may be added by fine needle aspiration cytology (FNAC). Conclusions. Breast imaging is still valuable in settings where health resources are limited. Single breast imaging (only either ultrasonography or mammography) is adequate for cancer diagnosis. It is therefore unnecessary to perform both imaging modalities. Accuracy of the diagnosis may be improved by FNAC, if available.
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Chaurasia, Vikas, Saurabh Pal, and BB Tiwari. "Prediction of benign and malignant breast cancer using data mining techniques." Journal of Algorithms & Computational Technology 12, no. 2 (February 20, 2018): 119–26. http://dx.doi.org/10.1177/1748301818756225.
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Breast cancer is the second most leading cancer occurring in women compared to all other cancers. Around 1.1 million cases were recorded in 2004. Observed rates of this cancer increase with industrialization and urbanization and also with facilities for early detection. It remains much more common in high-income countries but is now increasing rapidly in middle- and low-income countries including within Africa, much of Asia, and Latin America. Breast cancer is fatal in under half of all cases and is the leading cause of death from cancer in women, accounting for 16% of all cancer deaths worldwide. The objective of this research paper is to present a report on breast cancer where we took advantage of those available technological advancements to develop prediction models for breast cancer survivability. We used three popular data mining algorithms (Naïve Bayes, RBF Network, J48) to develop the prediction models using a large dataset (683 breast cancer cases). We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes. The results (based on average accuracy Breast Cancer dataset) indicated that the Naïve Bayes is the best predictor with 97.36% accuracy on the holdout sample (this prediction accuracy is better than any reported in the literature), RBF Network came out to be the second with 96.77% accuracy, J48 came out third with 93.41% accuracy.
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You, Chan-Ping, Man-Hong Leung, Wai-Chung Tsang, Ui-Soon Khoo, and Ho Tsoi. "Androgen Receptor as an Emerging Feasible Biomarker for Breast Cancer." Biomolecules 12, no. 1 (January 4, 2022): 72. http://dx.doi.org/10.3390/biom12010072.
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Biomarkers can be used for diagnosis, prognosis, and prediction in targeted therapy. The estrogen receptor α (ERα) and human epidermal growth factor receptor 2 (HER2) are standard biomarkers used in breast cancer for guiding disease treatment. The androgen receptor (AR), a nuclear hormone receptor, contributes to the development and progression of prostate tumors and other cancers. With increasing evidence to support that AR plays an essential role in breast cancer, AR has been considered a useful biomarker in breast cancer, depending on the context of breast cancer sub-types. The existing survival analyses suggest that AR acts as a tumor suppressor in ER + ve breast cancers, serving as a favorable prognostic marker. However, AR functions as a tumor promoter in ER-ve breast cancers, including HER2 + ve and triple-negative (TNBC) breast cancers, serving as a poor prognostic factor. AR has also been shown to be predictive of the potential of response to adjuvant hormonal therapy in ER + ve breast cancers and to neoadjuvant chemotherapy in TNBC. However, conflicting results do exist due to intrinsic molecular differences between tumors and the scoring method for AR positivity. Applying AR expression status to guide treatment in different breast cancer sub-types has been suggested. In the future, AR will be a feasible biomarker for breast cancer. Clinical trials using AR antagonists in breast cancer are active. Targeting AR alone or other therapeutic agents provides alternatives to existing therapy for breast cancer. Therefore, AR expression will be necessary if AR-targeted treatment is to be used.
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Lenga, Lukas, Simon Bernatz, Simon S. Martin, Christian Booz, Christine Solbach, Rotraud Mulert-Ernst, Thomas J. Vogl, and Doris Leithner. "Iodine Map Radiomics in Breast Cancer: Prediction of Metastatic Status." Cancers 13, no. 10 (May 18, 2021): 2431. http://dx.doi.org/10.3390/cancers13102431.
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Dual-energy CT (DECT) iodine maps enable quantification of iodine concentrations as a marker for tissue vascularization. We investigated whether iodine map radiomic features derived from staging DECT enable prediction of breast cancer metastatic status, and whether textural differences exist between primary breast cancers and metastases. Seventy-seven treatment-naïve patients with biopsy-proven breast cancers were included retrospectively (41 non-metastatic, 36 metastatic). Radiomic features including first-, second-, and higher-order metrics as well as shape descriptors were extracted from volumes of interest on iodine maps. Following principal component analysis, a multilayer perceptron artificial neural network (MLP-NN) was used for classification (70% of cases for training, 30% validation). Histopathology served as reference standard. MLP-NN predicted metastatic status with AUCs of up to 0.94, and accuracies of up to 92.6 in the training and 82.6 in the validation datasets. The separation of primary tumor and metastatic tissue yielded AUCs of up to 0.87, with accuracies of up to 82.8 in the training, and 85.7 in the validation dataset. DECT iodine map-based radiomic signatures may therefore predict metastatic status in breast cancer patients. In addition, microstructural differences between primary and metastatic breast cancer tissue may be reflected by differences in DECT radiomic features.
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Anand Kumar Gupta, Asadi Srinivasulu, Kamal Kant Hiran, Tarkeswar Barua, Goddindla Sreenivasulu, Sivaram Rajeyyagari, and Madhusudhana Subramanyam. "Early prediction and analysis of mammary glands cancer through deep learning approaches." World Journal of Advanced Engineering Technology and Sciences 6, no. 1 (May 30, 2022): 018–24. http://dx.doi.org/10.30574/wjaets.2022.6.1.0056.
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Cancer is the foremost cause behind the most death pace of people around the world. Cancer of breast is the primary reason for mortality among females. There have been various investigation or experimentation aimed at the discovery and interpretation of facts has been done on early expectation and discovery of breast cancer disease to begin treatment and increment the opportunity of endurance. Utmost research targets x-ray pictures of the breasts. Although, photographs of the breasts made by X-rays occasionally produces a threat of fake recognition which can compromise the medical status of infectious person. It’s crucial and import to locate opportunity techniques that might be simpler to put into effect and work with extraordinary records sets, inexpensive and safer, which could produce an extra dependable prognosis. This research journal recommends an associated prototype of numerous DLA (Deep Learning Algorithms) including ANN (Artificial Neural Network) and CNN (Convolutional Neural Networks) for efficient breast cancer detection and prediction. The research exploration utilizes the x-rays image database (as base research datasets) for prediction, detection, and diagnosis of breast cancer. This anticipated research prototype may be associated with several clinical examination data i.e. text, audio, image, video, blood, urine and many more.
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Agrawal, Rashmi. "Predictive Analysis Of Breast Cancer Using Machine Learning Techniques." Ingeniería Solidaria 15, no. 29 (September 16, 2019): 1–23. http://dx.doi.org/10.16925/2357-6014.2019.03.01.
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This paper is a product of the research Project “Predictive Analysis Of Breast Cancer Using Machine Learning Techniques” performed in Manav Rachna International Institute of Research and Studies, Faridabad in the year 2018.
Introduction: The present article is part of the effort to predict breast cancer which is a serious concern for women’s health.
Problem: Breast cancer is the most common type of cancer and has always been a threat to women’s lives. Early diagnosis requires an effective method to predict cancer to allow physicians to distinguish benign and malicious cancer. Researchers and scientists have been trying hard to find innovative methods to predict cancer.
Objective: The objective of this paper will be predictive analysis of breast cancer using various machine learning techniques like Naïve Bayes method, Linear Discriminant Analysis, K-Nearest Neighbors and Support Vector Machine method.
Methodology: Predictive data mining has become an instrument for scientists and researchers in the medical field. Predicting breast cancer at an early stage helps in better cure and treatment. KDD (Knowledge Discovery in Databases) is one of the most popular data mining methods used by medical researchers to identify the patterns and the relationship between variables and also helps in predicting the outcome of the disease based upon historical data of datasets.
Results: To select the best model for cancer prediction, accuracy of all models will be estimated and the best model will be selected.
Conclusion: This work seeks to predict the best technique with highest accuracy for breast cancer.
Originality: This research has been performed using R and the dataset taken from UCI machine learning repository.
Limitations: The lack of exact information provided by data.
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Ravi Shankar, J. S., S. Nithish, M. Nithish Babu, R. Karthik, and A. Shahid Afridi. "Breast Cancer Prediction using Decision Tree." Journal of Physics: Conference Series 1916, no. 1 (May 1, 2021): 012069. http://dx.doi.org/10.1088/1742-6596/1916/1/012069.
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Neskovic-Konstantinovic, Zora, Snezana Susnjar, Ljiljana Stamatovic, Suzana Vasovic, and Dragica Nikolic-Vukosavljevic. "Breast cancer biomarkers: Prognosis and prediction." Archive of Oncology 10, no. 3 (2002): 153–54. http://dx.doi.org/10.2298/aoo0203153n.
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Manikandan, G., B. Karthikeyan, P. Rajendiran, R. Harish, T. Prathyusha, and V. Sethu. "Breast Cancer Prediction Using Ensemble Techniques." Indian Journal of Public Health Research & Development 10, no. 7 (2019): 183. http://dx.doi.org/10.5958/0976-5506.2019.01559.6.
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Gautherie, Michel, and Charles M. Gros. "Breast thermography and cancer risk prediction." Cancer 45, no. 1 (June 28, 2006): 51–56. http://dx.doi.org/10.1002/cncr.2820450110.
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