Статті в журналах: "HEART DISEASE PREDICTION"

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Muthulakshmi, P., M. Parveen, and P. Rajeswari. "Prediction of Heart Disease using Ensemble Learning." Indian Journal Of Science And Technology 16, no. 20 (May 27, 2023): 1469–76. http://dx.doi.org/10.17485/ijst/v16i20.2279.

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Lu, Yi. "Heart Disease Prediction Model based on Prophet." Highlights in Science, Engineering and Technology 39 (April 1, 2023): 1035–40. http://dx.doi.org/10.54097/hset.v39i.6700.

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Heart disease is one of the major causes of death for people of all races, genders, and nationalities. In the United States, for instance, heart disease causes more than 600,000 deaths every year and is the largest leading cause of death in 2020. A reliable heart diseases mortality prediction model could acknowledge the patients’ medical professionals that the heart disease risk level of the specific group. This approach is significant in preventing further increases in heart disease mortality rates worldwide. Nowadays, multiple Machine Learning (ML) models, including hybrid models produced impressive predictions and realized that newly developed ML models might provide new perspectives on heart disease predictions. In this paper, we introduced the Facebook Prophet model (FB Prophet model), a time series prediction tool that could present seasonality in its result, since studies point out that heart disease mortality rate also shows seasonality. We produced an accuracy of approximately 94 % in predicting weekly heart disease mortality numbers in specific states. Furthermore, we explored the effects that external factors, ambient temperature, have on heart disease, and utilize this relationship in improving model accuracy.

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Rani, K. Sandhya, M. Sai Manoj, and G. Suguna Mani. "A Heart Disease Prediction Model using Logistic Regression." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1463–66. http://dx.doi.org/10.31142/ijtsrd11401.

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4

Vinothini, S., Ishaan Singh, Sujaya Pradhan, and Vipul Sharma. "Heart Disease Prediction." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 750. http://dx.doi.org/10.14419/ijet.v7i3.12.16494.

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Machine learning algorithm are used to produce new pattern from compound data set. To cluster the patient heart condition to check whether his /her heart normal or stressed or highly stressed k-means clustering algorithm is applied on the patient dataset. From the results of clustering ,it is hard to elucidate and to obtain the required conclusion from these clusters. Hence another algorithm, the decision tree, is used for the exposition of the clusters of . In this work, integration of decision tree with the help of k-means algorithm is aimed. Another learning technique such as SVM and Logistics regression is used. Heart disease prediction results from SVM and Logistics regression were compared.

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Kumar, Prof K. Senthil. "HEART DISEASE PREDICTION USING MACHINE LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (December 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem27570.

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Heart disease is a major cause of death worldwide, making early diagnosis and prevention essential. Predictive models have gained significant attention in recent years, with several algorithms being employed to develop these models. However, there are challenges in implementing heart disease prediction models, including data quality, model accuracy, ethical concerns, and limited data. Therefore, this project aims to develop a heart disease prediction model and analyse different algorithms used in disease prediction. In order to increase the predictive accuracy of machine learning algorithms, this study compares six algorithms, including KNN (K-Nearest Neighbour), Decision Tree, Random Forest, Support Vector Machines, Logistic Regression, and Neural Network. 13 attributes, including age, sex, and cholesterol, are used, and ensemble methods like boosting and bagging are used. The accuracy, recall, f1 score, and precision of each algorithm are calculated to determine the most accurate model. Additionally, this study identifies the limitations of heart disease prediction models and their implications for patient diagnosis and treatment, by developing and analysing heart disease prediction models. In conclusion, while heart disease prediction models have the potential to be financially feasible and be useful in the future, their current limitations and challenges mean that they cannot be relied upon as the sole means of diagnosis or treatment decisions Key Words: Heart Diseases, Machine Learning Algorithms, Logistic Regression, Random Forest, Decision Tree.

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Bavani, B., S. Nirmala Sugirtha Rajini, M. S. Josephine, and V. Prasannakumari. "Heart Disease Prediction System based on Decision Tree Classifier." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (October 31, 2019): 1232–37. http://dx.doi.org/10.5373/jardcs/v11sp10/20192968.

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Ahmad, Bamanga Mahmud, Ahmadu Asabe Sandra, Musa Yusuf Malgwi, and Dahiru I. Sajoh. "Ensemble model for Heart Disease Prediction." Science Progress and Research 1, no. 4 (October 5, 2021): 268–80. http://dx.doi.org/10.52152/spr/2021.145.

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For the identification and prediction of different diseases, machine learning techniques are commonly used in clinical decision support systems. Since heart disease is the leading cause of death for both men and women around the world. Heart is one of the essential parts of human body, therefore, it is one of the most critical concerns in the medical domain, and several researchers have developed intelligent medical devices to support the systems and further to enhance the ability to diagnose and predict heart diseases. However, there are few studies that look at the capabilities of ensemble methods in developing a heart disease detection and prediction model. In this study, the researchers assessed that how to use ensemble model, which proposes a more stable performance than the use of base learning algorithm and these leads to better results than other heart disease prediction models. The University of California, Irvine (UCI) Machine Learning Repository archive was used to extract patient heart disease data records. To achieve the aim of this study, the researcher developed the meta-algorithm. The ensemble model is a superior solution in terms of high predictive accuracy and diagnostics output reliability, as per the results of the experiments. An ensemble heart disease prediction model is also presented in this work as a valuable, cost-effective, and timely predictive option with a user-friendly graphical user interface that is scalable and expandable. From the finding, the researcher suggests that Bagging is the best ensemble classifier to be adopted as the extended algorithm that has the high prediction probability score in the implementation of heart disease prediction.

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Khan, Sundas Naqeeb, Nazri Mohd Nawi, Asim Shahzad, Arif Ullah, Muhammad Faheem Mushtaq, Jamaluddin Mir, and Muhammad Aamir. "Comparative Analysis for Heart Disease Prediction." JOIV : International Journal on Informatics Visualization 1, no. 4-2 (November 15, 2017): 227. http://dx.doi.org/10.30630/joiv.1.4-2.66.

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Today, heart diseases have become one of the leading causes of deaths in nationwide. The best prevention for this disease is to have an early system that can predict the early symptoms which can save more life. Recently research in data mining had gained a lot of attention and had been used in different kind of applications including in medical. The use of data mining techniques can help researchers in predicting the probability of getting heart diseases among susceptible patients. Among prior studies, several researchers articulated their efforts for finding a best possible technique for heart disease prediction model. This study aims to draw a comparison among different algorithms used to predict heart diseases. The results of this paper will helps towards developing an understanding of the recent methodologies used for heart disease prediction models. This paper presents analysis results of significant data mining techniques that can be used in developing highly accurate and efficient prediction model which will help doctors in reducing the number of deaths cause by heart disease.

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Pati, Abhilash, Manoranjan Parhi, and Binod Kumar Pattanayak. "IHDPM: an integrated heart disease prediction model for heart disease prediction." International Journal of Medical Engineering and Informatics 14, no. 6 (2022): 1. http://dx.doi.org/10.1504/ijmei.2022.10044903.

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Pati, Abhilash, Manoranjan Parhi, and Binod Kumar Pattanayak. "IHDPM: an integrated heart disease prediction model for heart disease prediction." International Journal of Medical Engineering and Informatics 14, no. 6 (2022): 564. http://dx.doi.org/10.1504/ijmei.2022.126526.

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11

Yuda Syahidin, Aditya Pratama Ismail, and Fawwaz Nafis Siraj. "Application of Artificial Neural Network Algorithms to Heart Disease Prediction Models with Python Programming." Jurnal E-Komtek (Elektro-Komputer-Teknik) 6, no. 2 (December 31, 2022): 292–302. http://dx.doi.org/10.37339/e-komtek.v6i2.932.

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Heart disease is one of the deadliest diseases in and is the number one killer in the world so many studies are carried out to contribute to predicting a person's heart disease. This study aims to help create an early heart disease prediction model from the UCI Machine Learning Repository dataset. The method proposed in this study is a deep learning technique that applies an artificial neural network algorithm with a hidden layer technique in making a heart disease prediction model. This research stage found problems in improving the accuracy of the datasets used by dealing with problems in pre-processing data, such as missing data and determining the form of data correlation. The model was then tested through a heart disease dataset and yielded 90% accuracy. With the creation of this prediction model with python programming, it is hoped that in addition to helping to make disease predictions, it can also provide further innovations in data science in the health sector.

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12

K.S, Anjana. "Heart Disease Prediction System." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 1861–65. http://dx.doi.org/10.22214/ijraset.2021.38230.

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Abstract: Heart diseases are the one of the primary reasons of human death today. There are many recent technologies are used to assist the medical professionals and doctors in the prediction of heart disease in the early stage. Prediction of heart disease is a critical challenge in the area of clinical data analysis. This paper introduces a technique to detect arrhythmia, which is a representative type of cardio vascular diseases. Arrhythmia refers to any irregular change from the normal heart rhythms, means that your heart beats too quickly, too slowly, or with an irregular pattern. The Electro Cardiogram (ECG) is used as an input for the arrhythmia detection. It displays the rhythm and status of the heart. This paper propose an effective ECG arrhythmia classification approach based on a deep convolutional neural network (CNN), which has lately demonstrated remarkable performance in the field of machine learning. It perform the classification without any manual pre-processing of the ECG signals such as noise filtering, feature extraction, and feature reduction. Keywords: Arrhythmia, ECG, deep learning, CNN, ResNet

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13

H, Tanyildizi Kökkülünk. "Prediction of Heart Disease Using Machine Learning with Data Mining." Physical Science & Biophysics Journal 7, no. 1 (January 5, 2023): 1–6. http://dx.doi.org/10.23880/psbj-16000228.

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Aim: In this study, it was aimed to make a categorical estimation of the absent/presence of heart disease by using some parameters (age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thalach) of healthy and heart disease individuals. Material and Methods: The classification was obtained with multiple linear regression (MLR) of machine learning in the R Studio program. Machine learning has been improved by selecting parameters that have a high contribution to the prediction by using the Akaike information criterion. Results: The classification was performed using the biomarkers from glm.fit.1, which produced the lowest AIC value (237.48). The accuracy of the MLR model used was 88%, the precision was 93%, the sensitivity was 86%, and the specificity was 91%. It was found that age data from biomarkers contributed little to the prediction. Conclusion: MLR is a preferable method for categorical disease classification.

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Kumar, D. Suresh, SK Abdul Kareem, K. Roopesh, and P. Aneesh. "Heart Disease Prediction Model." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (March 31, 2023): 1145–48. http://dx.doi.org/10.22214/ijraset.2023.49570.

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Abstract: Heart disease is a leading cause of death worldwide. Early prediction of heart disease can save many lives. Data mining techniques have been widely used to predict heart disease. In this paper, we present a comprehensive study on heart disease prediction using data mining techniques. We analyse the various data mining algorithms that have been used in the literature for heart disease prediction. We also evaluate the performance of these algorithms using several metrics such as accuracy, precision, recall, and F1 score. We conclude that data mining techniques are useful in heart disease prediction and can help in early diagnosis of heart disease.

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Salunkhe, Shubham Shankar, Sakshi Sunil Shinde, Pruthviraj Balasaheb Zambare, and Prof S. K. Godase. "Multiple Disease Prediction Using Machine Learning." International Journal of Research In Science & Engineering, no. 11 (September 30, 2021): 43–49. http://dx.doi.org/10.55529/ijrise.11.43.49.

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There are several techniques in machine learning that can perform predictive analytics on large amounts of data across industries. Predictive analytics in healthcare is a difficult task, but it can ultimately help practitioners in making timely decisions regarding the health and treatment of patients based on massive data. Diseases such as breast cancer, diabetes and heart disease outbreaks cause many deaths worldwide, but most of these deaths are due to a lack of early disease control. The above problem occurs due to inadequate medical infrastructure and low ratio of doctors to population. Statistics clearly show the same, WHO advises, doctor to patient ratio is 1:1000, while doctor to population ratio in India is 1:1456, this shows shortage of doctors. Diseases related to heart, cancer and diabetes can pose a potential threat to humanity if not detected early. Therefore, early recognition and diagnosis of these diseases can save many lives. This thesis is all about predicting diseases that are harmful using machine learning classification algorithms. Breast cancer, heart and diabetes are included in this work. To make this work seamless and usable by the general public, our team has created a medical test web application that predicts various diseases using the concept of machine learning. In this work, we aim to develop a machine learning-based prediction concept for various diseases such as breast cancer, diabetes, and heart disease.

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Verma, Raunak, Shashank Tandon, and Mr Vinayak. "Heart Disease Prediction using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 1872–76. http://dx.doi.org/10.22214/ijraset.2022.42687.

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Abstract: The term "heart disease" refers to any heart disease or condition that can cause heart problems. Cardiovascular disease (CVD) is the leading cause of death worldwide, taking many lives each year. CVD is a group of cardiovascular diseases and includes heart disease, cerebrovascular disease, rheumatic heart disease and other conditions. According to the World Health Organization (WHO), more than 17.9 million people worldwide die each year from coronary heart disease. If we take the example of India, every year the number of deaths due to heart disease has increased. Studies show that, from 2014 to 2019 the number of deaths from heart disease increased by 53%. Many threatening factors such as personal and work habits and genetic predisposition are major causes of heart disease. A variety of harmful habits such as smoking, alcohol and caffeine overdose, stress, and inactivity as well as other physical factors such as obesity, high blood pressure, high blood cholesterol, and pre-existing heart conditions are the main causes of heart disease. Over time, these harmful substances cause changes in the heart and blood vessels that can lead to heart attacks and strokes. Therefore, prevention of heart disease is very important to prevent these dangerous events and other potential complications of heart disease. Machine learning is a flexible part of AI that helps predict heart disease. In this research work, we will use the UCI database with 14 attributes to predict heart disease. The main goal of this study is to use ML algorithms to improve the heart disease prediction system and to more accurately predict these diseases in patients, thereby reducing the number of deaths by alerting patients. Keywords: Heart Diseases, Classification Algorithms, Machine Learning, UCI dataset.

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Nanekar, Ganesh. "Heart Disease Prediction using Neural Network." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 20, 2021): 1907–10. http://dx.doi.org/10.22214/ijraset.2021.35418.

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Heart is the next major organ comparing to brain which has more priority in Human body. It pumps the blood and supplies to all organs of the whole body. Prediction of occurrences of heart diseases in medical field is significant work. Data analytics is useful for prediction from more information and it helps medical Centre to predict of various disease. Huge amount of patient related data is maintained on monthly basis. The stored data can be useful for source of predicting the occurrence of future disease. Some of the data mining and machine learning techniques are used to predict the heart disease, such as Decision tree, Fuzzy Logic, K-Nearest Neighbor (KNN), Naïve Bayes and Support Vector Machine (SVM). This paper provides an insight of the existing algorithms and implements hybrid algorithms to improve accuracy significantly.

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Chauhan, Shivam. "Prognosis of Heart Disease using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 812–18. http://dx.doi.org/10.22214/ijraset.2023.53752.

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Abstract: This article presents the prediction of the heart diseases by using the machine learning algorithm. One of the major causes of morbidity in the world's population is the prediction of heart attacks. Cardiovascular disease is a very essential disease that is included in the clinical data analysis as one of the most crucial sections for the prediction. In this study, we describe a technique to heart attack prediction that uses machine learning to analyse several risk factors and make predictions about heart attacks. Heart disease cases are rising quickly every day, thus it's crucial and worrisome to predict any potential illnesses in advance. This diagnosis is a challenging task that requires accuracy and efficiency. The primary focus of the research paper is on which patients, given certain medical characteristics, are more likely to suffer heart disease. Using the patient's medical history, we developed a system to determine if a heart disease diagnosis is likely or not for the patient

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Rani, K. Sandhya, M. Sai Chaitanya, and G. Sai Kiran. "A Heart Disease Prediction Model using Logistic Regression By Cleveland DataBase." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1467–70. http://dx.doi.org/10.31142/ijtsrd11402.

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Dileep, Pulugu, Kunjam Nageswara Rao, and Prajna Bodapati. "Enhancing Heart Disease Prediction Models with Feature Selection and Ensemble Methods." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11-SPECIAL ISSUE (February 20, 2019): 400–411. http://dx.doi.org/10.5373/jardcs/v11sp11/20193048.

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N, Sathvik, Shrinivas ., Siddesh B H, and Vinay J. "Heart Disease Prediction Using ML." International Journal of Innovative Research in Advanced Engineering 10, no. 05 (May 31, 2023): 134–38. http://dx.doi.org/10.26562/ijirae.2023.v1005.01.

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Machine Learning is used across many ranges around the world. The healthcare industry is no exclusion. Machine Learning can play an essential role in predicting presence/absence of locomotors disorders, Heart diseases and more. Such information, if predicted well in advance, can provide important intuitions to doctors who can then adapt their diagnosis and dealing per patient basis. We work on predicting possible Heart Diseases in people using Machine Learning algorithms. In this project we perform the comparative analysis of classifiers like decision tree, Naïve Bayes, Logistic Regression, SVM and Random Forest and we propose an ensemble classifier which perform hybrid classification by taking strong and weak classifiers since it can have multiple number of samples for training and validating the data so we perform the analysis of existing classifier and proposed classifier like Ada-boost and XG-boost which can give the better accuracy and predictive analysis.

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Kim, Jae Kwon, and Sanggil Kang. "Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis." Journal of Healthcare Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/2780501.

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Background. Of the machine learning techniques used in predicting coronary heart disease (CHD), neural network (NN) is popularly used to improve performance accuracy. Objective. Even though NN-based systems provide meaningful results based on clinical experiments, medical experts are not satisfied with their predictive performances because NN is trained in a “black-box” style. Method. We sought to devise an NN-based prediction of CHD risk using feature correlation analysis (NN-FCA) using two stages. First, the feature selection stage, which makes features acceding to the importance in predicting CHD risk, is ranked, and second, the feature correlation analysis stage, during which one learns about the existence of correlations between feature relations and the data of each NN predictor output, is determined. Result. Of the 4146 individuals in the Korean dataset evaluated, 3031 had low CHD risk and 1115 had CHD high risk. The area under the receiver operating characteristic (ROC) curve of the proposed model (0.749 ± 0.010) was larger than the Framingham risk score (FRS) (0.393 ± 0.010). Conclusions. The proposed NN-FCA, which utilizes feature correlation analysis, was found to be better than FRS in terms of CHD risk prediction. Furthermore, the proposed model resulted in a larger ROC curve and more accurate predictions of CHD risk in the Korean population than the FRS.

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Salunke, Tanmay, Pavankumar Jagade, Shreyash Pawar, Pavan Rathod, and Prof Neha Ghawate. "Heart Disease Detection using Hybrid Machine Learning and IoT (Software Based)." April-May 2023, no. 33 (April 11, 2023): 10–13. http://dx.doi.org/10.55529/jaimlnn.33.10.13.

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heart disease is a leading cause of mortality worldwide, presenting a critical challenge for clinical data analysis in predicting cardiovascular disease. Machine learning (ML) has shown promise in assisting decision-making and predictions based on the large amounts of data generated by the healthcare industry. Advancements in the Internet of Things (IoT) have opened new avenues for the application of ML techniques in diverse domains. However, the current literature provides only a limited perspective on predicting heart disease with ML techniques. To address this gap, we propose a novel approach that leverages ML techniques to identify significant features that can improve the accuracy of heart disease prediction. By utilizing a variety of feature combinations and established classification techniques, our prediction model achieves a superior level of performance with an accuracy rate of 88.7% for predicting heart disease. The hybrid random forest with a linear model (HRFLM) was found to be particularly effective in achieving these results.

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B, Shadaksharappa. "PREDICTION OF CARDIOVASCULAR DISEASE BY MACHINE LEARNING AND IOT." International Research Journal of Computer Science 09, no. 04 (April 30, 2022): 101–4. http://dx.doi.org/10.26562/irjcs.2022.v0904.006.

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Heart disease is one of the most significant causes of mortality in the world today. Prediction of cardiovascular disease is a critical challenge in the area of clinical data analysis. Machine learning (ML) has been shown to be effective in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry. We have also seen ML techniques being used in recent developments in different areas of the Internet of Things (IoT). Various studies give only a glimpse into predicting heart disease with ML techniques. In this paper, we propose a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease. The prediction model is introduced with different combinations of features and several known classification techniques. We produce an enhanced performance level through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM). Based on the sensors connected with the human body the ML algorithm forecast the future health status.

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Bargavi, Dr S. K. Manju, and Deepak Kumar Tigga. "Prediction for Effective Heart Disease System." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2746–59. http://dx.doi.org/10.22214/ijraset.2022.41892.

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Abstract: The health care industries gather large quantities of records that include a few hidden information, which is beneficial for making powerful choices. For imparting suitable effects and making powerful choices on records, a few superior records mining strategies are used. In this study, a Heart Disease Prediction System (HDPS) evolved the use of Naive Bayes and Decision Tree algorithms for predicting the danger stage of a coronary heart ailment. The device makes use of 15 scientific parameters along with age, sex, blood pressure, cholesterol, and weight problems for prediction. The HDPS predicts the chance of sufferers getting coronary heart ailment. It permits substantial knowledge. Relationships among scientific elements associated with coronary heart ailment and patterns, to be established. We have hired the multilayer perceptron neural community with backpropagation because of the education algorithm. The acquired effects have illustrated that the designed diagnostic device can efficiently predict the dangerous stage of coronary heart disease.

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Sada, Wasilah, and Celinus Kiyea. "Enhancing Heart Disease Prediction Using Ensemble Techniques." SLU Journal of Science and Technology 4, no. 1&2 (July 20, 2022): 130–46. http://dx.doi.org/10.56471/slujst.v4i.277.

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Background: Cardiovascular diseases are recognized generally to be among the number one illnesscausing death across the globe. Predicting heart disease using a computer-aided technique makes it easier for medical practitioners to diagnose and thereby savinglives andreducingcosts. Feature selection has become an essential component for developing Machinelearning models. It chooses the most relevant features from the available dataset,thereby shortening the training period, making the model easier to train, improving generalization and decreasing overfitting without necessarily compromising the system’s accuracy. Aim:The purpose of this work is to design and build an optimal model forthe prediction of heart diseases,especially at an early stage by considering certain features that are most relevant forthe prediction without compromising the system’s accuracy. Method: The Cleveland UCI dataset with 303 instances wereused in trainingthe model and the findings showthat selectKBest is an effective tool in improving the prediction of heart diseases. The performance metrics Accuracy, Sensitivity, Precision were measured.Results: the study found that when hybridizing k-Nearest Neighbor Bagging, Decision TreeBagging, Gradient Boosting generated the highest accuracy of 90%, 85% and 88% respectively.

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Mohammad, Farah, and Saad Al-Ahmadi. "WT-CNN: A Hybrid Machine Learning Model for Heart Disease Prediction." Mathematics 11, no. 22 (November 17, 2023): 4681. http://dx.doi.org/10.3390/math11224681.

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Heart disease remains a predominant health challenge, being the leading cause of death worldwide. According to the World Health Organization (WHO), cardiovascular diseases (CVDs) take an estimated 17.9 million lives each year, accounting for 32% of all global deaths. Thus, there is a global health concern necessitating accurate prediction models for timely intervention. Several data mining techniques are used by researchers to help healthcare professionals to predict heart disease. However, the traditional machine learning models for predicting heart disease often struggle with handling imbalanced datasets. Moreover, when prediction is on the bases of complex data like ECG, feature extraction and selecting the most pertinent features that accurately represent the underlying pathophysiological conditions without succumbing to overfitting is also a challenge. In this paper, a continuous wavelet transformation and convolutional neural network-based hybrid model abbreviated as WT-CNN is proposed. The key phases of WT-CNN are ECG data collection, preprocessing, RUSBoost-based data balancing, CWT-based feature extraction, and CNN-based final prediction. Through extensive experimentation and evaluation, the proposed model achieves an exceptional accuracy of 97.2% in predicting heart disease. The experimental results show that the approach improves classification accuracy compared to other classification approaches and that the presented model can be successfully used by healthcare professionals for predicting heart disease. Furthermore, this work can have a potential impact on improving heart disease prediction and ultimately enhancing patient lifestyle.

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Chattar, Mrs Shital P. "IOT based Heart Disease Prediction." International Journal for Research in Applied Science and Engineering Technology 9, no. 2 (February 28, 2021): 123–24. http://dx.doi.org/10.22214/ijraset.2021.32976.

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Nagaprasad, S., T. Pushpalatha, and S. Naga Lakshmi. "Heart Disease Prediction Propagation approach." International Journal of Machine Learning and Networked Collaborative Engineering 4, no. 2 (October 24, 2020): 72–77. http://dx.doi.org/10.30991/ijmlnce.2020v04i02.003.

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Baad, Bhavana. "Heart Disease Prediction and Detection." International Journal for Research in Applied Science and Engineering Technology 7, no. 4 (April 30, 2019): 2293–99. http://dx.doi.org/10.22214/ijraset.2019.4415.

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Purushottam, Kanak Saxena, and Richa Sharma. "Efficient Heart Disease Prediction System." Procedia Computer Science 85 (2016): 962–69. http://dx.doi.org/10.1016/j.procs.2016.05.288.

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Gupta, Vaibhav, and Dr Pallavi Murghai Goel. "Heart Disease Prediction Using ML." International Journal of Computer Science and Engineering 7, no. 6 (June 25, 2020): 17–19. http://dx.doi.org/10.14445/23488387/ijcse-v7i6p105.

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Dubey, Shatendra Kumar, Dr Sitesh Sinha, and Dr Anurag Jain. "Heart Disease Prediction Classification using Machine Learning." International Journal of Inventive Engineering and Sciences 10, no. 11 (November 30, 2023): 1–6. http://dx.doi.org/10.35940/ijies.b4321.11101123.

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Heart disease is a leading cause of mortality worldwide, and early detection and accurate prediction of heart disease can significantly improve patient outcomes. Machine learning techniques have shown great promise in assisting healthcare professionals in diagnosing and predicting heart disease. The diagnosis and prognosis of heart disease must be improved, refined, and accurate, because a small mistake can cause weakness or death. According to a recent World Health Organization study, 17.5 million people die each year. By 2030, this number will increase to 75 million.[2] This document explains how to enable online KSRM capabilities. The KSRM smart system allows users to report heart-related problems. This research paper aims to explore the use of machine learning algorithms for effective heart disease prediction classification with Ada boost for improve the accuracy of algorithm.

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Duraisamy, Balakrishnan, Rakesh Sunku, Krithik Selvaraj, Vishnu Vardhan Reddy Pilla, and Manoj Sanikala. "Heart disease prediction using support vector machine." Multidisciplinary Science Journal 6 (December 15, 2023): 2024ss0104. http://dx.doi.org/10.31893/multiscience.2024ss0104.

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Heart disease prediction through online consultation using machine learning refers to the application of advanced algorithms and techniques to analyze medical data collected during online consultations to predict the likelihood of an individual developing heart disease. Machine learning models are trained using historical data that includes various risk factors such as age, gender, blood pressure, cholesterol levels, and medical history. These models then utilize the input provided by patients during online consultations, such as symptoms, lifestyle habits, and additional medical tests, to generate personalized predictions about the probability of heart disease occurrence. By leveraging the power of machine learning, this approach aims to assist healthcare professionals in making more accurate diagnoses and providing timely recommendations for preventive measures or further medical intervention, ultimately improving patient outcomes and reducing the burden on healthcare systems. In this paper, a machine learning technique called Support Vector Machine (SVM) is used for heart disease prediction. Heart disease prediction through online consultation using SVM involves utilizing SVM as a machine learning algorithm to predict the likelihood of an individual having heart disease based on their consultation information provided online.

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Zhang, Dengqing, Yunyi Chen, Yuxuan Chen, Shengyi Ye, Wenyu Cai, Junxue Jiang, Yechuan Xu, Gongfeng Zheng, and Ming Chen. "Heart Disease Prediction Based on the Embedded Feature Selection Method and Deep Neural Network." Journal of Healthcare Engineering 2021 (September 29, 2021): 1–9. http://dx.doi.org/10.1155/2021/6260022.

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In recent decades, heart disease threatens people’s health seriously because of its prevalence and high risk of death. Therefore, predicting heart disease through some simple physical indicators obtained from the regular physical examination at an early stage has become a valuable subject. Clinically, it is essential to be sensitive to these indicators related to heart disease to make predictions and provide a reliable basis for further diagnosis. However, the large amount of data makes manual analysis and prediction taxing and arduous. Our research aims to predict heart disease both accurately and quickly through various indicators of the body. In this paper, a novel heart disease prediction model is given. We propose a heart disease prediction algorithm that combines the embedded feature selection method and deep neural networks. This embedded feature selection method is based on the LinearSVC algorithm, using the L1 norm as a penalty item to choose a subset of features significantly associated with heart disease. These features are fed into the deep neural network we built. The weight of the network is initialized with the He initializer to prevent gradient varnishing or explosion so that the predictor can have a better performance. Our model is tested on the heart disease dataset obtained from Kaggle. Some indicators including accuracy, recall, precision, and F1-score are calculated to evaluate the predictor, and the results show that our model achieves 98.56%, 99.35%, 97.84%, and 0.983, respectively, and the average AUC score of the model reaches 0.983, confirming that the method we proposed is efficient and reliable for predicting heart disease.

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Nikleshwar, E., D. Sindhuja, T. Sai Charan, and r. Mohammed Mahabbob Basha. "Heart Disease Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (January 31, 2023): 946–50. http://dx.doi.org/10.22214/ijraset.2023.48562.

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bstract: Cardiovascular disease is a major health burden worldwide in the 21st century. Human services consumption is overpowering national and corporate spending plans because of asymptomatic infections including cardiovascular ailments. Consequently, there’s an urgent requirement for early location and treatment of such ailments. The information which is gathered by data analysis of hospitals is utilized by applying different blends of calculations and algorithms for the early-stage prediction of Cardiovascular ailments. Machine Learning is one among the slanting innovations utilized in numerous circles far and wide including the medicinal services application for predicting illnesses. The proposed project is predicated on a typical machine learning algorithm k-nearest algorithm.

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Anshori, Mochammad, and M. Syauqi Haris. "Predicting Heart Disease using Logistic Regression." Knowledge Engineering and Data Science 5, no. 2 (December 30, 2022): 188. http://dx.doi.org/10.17977/um018v5i22022p188-196.

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A common risk of death is caused by heart disease. It is critical in the field of medicine to be able to diagnose cardiac disease in order to adequately prevent and treat patients. The most accurate method of prediction has the potential to both extend the patient's life and reduce the severity of their cardiac disease. The use of machine learning is one approach that may be taken to generate predictions. In this study, patient medical record information was used in conjunction with an algorithm for logistic regression in order to make heart disease diagnoses. The outcomes of the logistic regression have been utilized to achieve a high level of accuracy in the prediction of heart disease. To get the model coefficients needed for the equation, the experiment uses an iterative form of the logistic regression test. Iteration 14 produced the best results, with an accuracy of 81.3495% and an average calculation time of 0.020 seconds. The best iteration was reached at that point. The percentage of space that lies beneath the ROC curve is 89.36%. The findings of this study have significant implications for the field of heart disease prediction and can contribute to improved patient care and outcomes. Accurate predictions obtained through logistic regression can guide healthcare professionals in identifying individuals at risk and implementing preventive measures or tailored treatment plans. The computational efficiency of the model further enhances its applicability in real-time decision support systems.

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Qu, Shuang, and Jing Zhu. "A Nomogram for Predicting Cardiovascular Diseases in Chronic Obstructive Pulmonary Disease Patients." Journal of Healthcare Engineering 2022 (October 18, 2022): 1–10. http://dx.doi.org/10.1155/2022/6394290.

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Cardiovascular diseases (CVDs) are the most common comorbidities in the chronic obstructive pulmonary disease (COPD), which increase the risk of hospitalization, length of stay, and death in COPD patients. This study aimed to identify the predictors for CVDs in COPD patients and construct a prediction model based on these predictors. In total, 1022 COPD patients in National Health and Nutrition Examination Surveys (NHANES) were involved in the cross-sectional study. All subjects were randomly divided into the training set (n = 709) and testing set (n = 313). The differences before and after the manipulation of the missing data were compared via sensitivity analysis. Univariate and multivariable analyses were employed to screen the predictors of CVDs in COPD patients. The performance of the prediction model was evaluated via the area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and calibration. Subgroup analysis was performed in patients using different COPD diagnosis methods and patients smoking or not smoking in the testing set. We found that male, older age, a smoking history, overweight, a history of blood transfusion, a history of heart disease in close relatives, higher levels of white blood cell (WBC), and monocyte (MONO) were associated with the increased risk of CVDs in COPD patients. Higher levels of platelets (PLT) and lymphocyte (LYM) were associated with reduced risk of CVDs in COPD patients. A prediction model for the risk of CVDs in COPD patients was established based on predictors including gender, age, a smoking history, BMI, a history of blood transfusion, a history of heart disease in close relatives, WBC, MONO, PLT, and LYM. The AUC value of the prediction model was 0.75 (95% CI: 0.71–0.79) in the training set and 0.79 (95%CI: 0.73–0.85) in the testing set. The prediction model established showed good predictive performance in predicting CVDs in COPD patients.

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Randhawan, Bhagyesh, Ritesh Jagtap, Amruta Bhilawade, and Durgesh Chaure. "Heart Disease Prediction Using Logistic Regression Algorithm." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2667–72. http://dx.doi.org/10.22214/ijraset.2022.41860.

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Abstract: Heart - a primary organ of our circulatory system. Which keeps blood that's full of oxygen circulating throughout your body. From past two decades Heart-disease remained as a leading cause of death at global level. Statistics illustrate the lethality of cardiovascular disease by showing the percentage of deaths caused by heart attacks worldwide. Therefore, it is crucial to predict the condition as earliest as possible time. Cardiologist have limitations, they cannot predict heart disease risk to a high degree of accuracy. So, a reliable, accurate and feasible system is required to predict such diseases in time for proper treatment. In order to automate analysis of large and complex medical datasets, Machine Learning algorithms and techniques have been applied. Machine learning techniques have been increasingly used by researchers in the health care industry and by professionals to diagnose conditions related to the heart. A quick and efficient detection technique is needed to reduce the high death rate caused by heart diseases. Here, machine learning algorithms and data mining techniques play a very crucial role. Using machine learning algorithms, this research aims to predict the occurrence of heart disease in a patient. Keywords: Machine Learning, Supervised Learning, unsupervised Learning, Logistic Regression, Cardiovascular diseases

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Dhande, Bhavesh, Kartik Bamble, Sahil Chavan, and Tabassum Maktum. "Diabetes & Heart Disease Prediction Using Machine Learning." ITM Web of Conferences 44 (2022): 03057. http://dx.doi.org/10.1051/itmconf/20224403057.

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One of the root causes of mortality in today's world is the culmination of several heart disease and diabetes illnesses. In clinical data analysis, predicting multiple diseases is a significant challenge. The machine learning approach has proved to be functional in assisting in the decision-making and governing of large amounts of data generated by the healthcare field. The various experiments scratch the surface of machine learning to predict different diseases. The papers present a novel method for identifying significant features using machine learning techniques, which improves the diagnosis of multi-purpose disease prediction. The different features and many well-known classification methods are used to implement the prediction model to predict the heart disease and diabetes. The proposed method utilizes ensemble approach for achieving a higher degree of accuracy rates for by using classification algorithms and feature selection methods. The proposed method implements voting classifier that has sigmoid SVC, AdaBoost, and Decision tree algorithms. The paper also implements the traditional classifiers and presents the comparison of different models in terms of accuracy. The web application is also developed for users to avail its services very easily and make it convenient for their use, particularly in the prediction of heart and diabetes collectively.

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Nandal, Neha, Lipika Goel, and ROHIT TANWAR. "Machine learning-based heart attack prediction: A symptomatic heart attack prediction method and exploratory analysis." F1000Research 11 (September 29, 2022): 1126. http://dx.doi.org/10.12688/f1000research.123776.1.

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Background; Heart attack prediction is one of the serious causes of morbidity in the world’s population. The clinical data analysis includes a very crucial disease i.e., cardiovascular disease as one of the most important sections for the prediction. Data Science and machine learning (ML) can be very helpful in the prediction of heart attacks in which different risk factors like high blood pressure, high cholesterol, abnormal pulse rate, diabetes, etc... can be considered. The objective of this study is to optimize the prediction of heart disease using ML. Methods: In this paper, we are presenting a machine learning-based heart attack prediction (ML-HAP) method in which the analysis of different risk factors and prediction for heart attacks is done using ML approaches of Support Vector Machines, Logistic Regression, Naïve Bayes and XGBoost. The data of heart disease symptoms has been collected from the UCI ML Repository and analysis has been performed on the data using ML methods. The focus has been on optimizing the prediction on the basis of different parameters. Results: XGBoost provided the best prediction among the four. The Area under the curve achieved with XGBoost is .94 and Logistic Regression is .92. The prediction with ML models in identifying heart attack symptoms is highly efficient, especially with boosting algorithms. The prediction was done to evaluate accuracy, precision, recall, and area under the curve. ML models are being trained to perform optimized predictions. Conclusions: This prediction can help clinically in analyzing the risk factors of the disease and interpretation of the patient scenario. Boosting the algorithm provided promising results to predict symptoms of heart disease. It can further be optimized by working further on risk factors associated with this condition.

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Satish, Dr K. "Heart Disease Prediction using Machine Learning Algorithms." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (November 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem27405.

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Presently, health conditions are on the rise primarily due to lifestyle factors and genetic predispositions. Among these, heart disease has notably surged, posing a significant risk to people's lives. Each person possesses unique benchmarks for vital health indicators like blood pressure, cholesterol levels, and pulse rates. However, according to established medical standards, normal values typically fall within certain ranges: Blood pressure ideally registers at 120/90, cholesterol between 100-129 mg/dL, pulse rate around 72, fasting blood sugar level at 100 mg/dL, heart rate ranging from 60-100 beats per minute (bpm), normal ECG readings, and major vessel widths spanning from 25 mm (1 inch) in the aorta to a mere 8 μm in capillaries. This study explores various classification techniques employed to predict the risk levels of individuals based on parameters such as age, gender, blood pressure, cholesterol levels, and pulse rate. The "Disease Prediction" system relies on predictive modeling to anticipate a user's disease risk by analyzing symptoms provided as input. By evaluating user-input symptoms, the system computes the probability of specific diseases as an output. Disease prediction involves the implementation of five techniques: Naïve Bayes, KNN, Decision Tree, Linear Regression, and Random Forest Algorithms. These methods gauge the likelihood of an individual developing a particular ailment. Consequently, the collective average prediction accuracy probability stands at an impressive 83%. Keywords: Naïve Bayes, KNN, Decision Tree, Linear Regression & Random Forest.

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Amit Jain, Suresh Babu Dongala, and Aruna Kama. "Heart disease prediction using machine learning techniques." Open Access Research Journal of Engineering and Technology 3, no. 1 (July 30, 2022): 001–6. http://dx.doi.org/10.53022/oarjet.2022.3.1.0028.

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Heart diseases are commonly caused and when neglected becomes life threatening. So, early detection of the disease is very important and for diagnosis to save lives. There can be many parameters that are to be considered to predict the heart disease. Some of them are like age, cholesterol, blood pressure levels. Etc., here we are going to implement Machine Learning model to predict heart disease.

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Kumar, Prof Amit, Harshika Bansal, Ayush Jaiswal, and Sovit Kumar Gupta. "Early Disease Prediction using Ml." International Journal of Advanced Engineering and Nano Technology 10, no. 11 (November 30, 2023): 1–4. http://dx.doi.org/10.35940/ijaent.i9694.11101123.

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The approach employed in disease prediction using machine learning involves making forecasts about various diseases by utilizing symptoms provided by patients or other individuals. The supervised machine learning approaches called random forest classifier, KNN classifier, SVMs classifier are employed to forecast the disease. These algorithms are used to determine the disease's probability. Accurate medical data analysis helps with patient care and early disease identification as biomedical and healthcare data volumes rise. Diabetes, heart diseases are just a few of the illnesses we can forecast using linear regression and decision trees. Early detection is beneficial for determining the possibility of diabetes, heart disease.

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Al Khoir, M. Agus Badruzaman, and Sriyanto Sriyanto. "NEURAL NETWORK OPTIMIZATION WITH GENETIC ALGORITHM FOR HEART DISEASE PREDICTION." IJISCS (International Journal of Information System and Computer Science) 6, no. 2 (August 31, 2022): 89. http://dx.doi.org/10.56327/ijiscs.v6i2.1235.

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Coronary Heart Disease (CHD) is a contributor to the number 1 cause of death in the world besides cardiovascular disease. The tendency of Indonesian people who do not know and ignore coronary heart disease is a factor that causes Indonesia to be high a contributor to deaths caused by coronary heart disease. This research is expected to produce new predictions of heart disease using genetic optimization of neural networks with better prediction results and can obtain algorithms with new percentage values in predicting coronary heart disease. Genetic optimization of the neural network is used because the algorithm follows the human nervous system which has the characteristics of parallel processing, processing elements in large quantities, and fault tolerance. The results of the research carried out are the accuracy obtained by 82.18% and increased to 83.50% after using genetic algorithm optimization, from these results it can be concluded that the neural network algorithm can be better if it is supported by genetic algorithm optimization

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Shaikh, Rumana M. "Cardiovascular Diseases Prediction Using Machine Learning Algorithms." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (April 11, 2021): 1083–88. http://dx.doi.org/10.17762/turcomat.v12i6.2426.

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A broad variety of health conditions are involved in heart disease. Several illnesses and disorders come under the heart disease umbrella. Heart disease forms include: In arrhythmia, abnormality of the heart rhythm. Arteriosclerosis, Hardening of the arteries is atherosclerosis. Via cardiomyopathy, this disorder causes muscles in the heart to harden or grow weak. Defects of the congenital heart, heart abnormalities that are present at birth are congenital heart defects. Disease of the coronary arteries (CAD), the accumulation of plaque in the heart's arteries triggers CAD. It's called ischemic heart disease occasionally. Infections of the heart, bacteria, viruses, or parasites may trigger heart infections. Heart diseases namely arrhythmias, coronary heart disease, heart attacks, cardiomyopathy will be detect using the proposed algorithm in this paper. Here I compared three algorithms namely Restricted Boltzmann Machines, Deep Belief Networks and Convolutional Neural Networks for electrocardiogram (ECG) classification for heart disease.

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Tejaswini, K. "Heart Disease Prediction using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (July 31, 2023): 671–74. http://dx.doi.org/10.22214/ijraset.2023.54629.

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Abstract: Heart disease is a common, potentially fatal disorder that affects a lot of people all over the world. A thorough and timely diagnosis is essential for efficient treatment and management. By analyzing large datasets, deep learning algorithms have demonstrated considerable promise in the diagnosis of cardiac disease. Given the alarming prevalence of heart diseases and their substantial impact on mortality rates, researchers worldwide have dedicated substantial efforts to address this issue. They have approached heart diagnosis as a classification problem, employing data mining techniques to identify meaningful patterns. This project specifically focuses on supervised learning, employing various algorithms such as regression (including linear regression, support vector machine, and Poisson regression) and classification (including logistic regression, decision tree, random forest, and naive Bayes)

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DURRINGTON, P. N. "Methods for the prediction of coronary heart disease risk." Heart 85, no. 5 (May 1, 2001): 489–90. http://dx.doi.org/10.1136/heart.85.5.489.

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Liu, Songwei, Zhonghui Miao, and Anqiao Zhang. "Exploiting Neural Network for Heart Disease Probability Prediction." Applied and Computational Engineering 8, no. 1 (August 1, 2023): 136–41. http://dx.doi.org/10.54254/2755-2721/8/20230102.

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In general, doctors determine the presence of heart disease through clinical evaluation and pathological data, and the diagnosis process is complex and inefficient. Based on the above situation, professionals are committed to researching efficient and accurate methods for predicting heart disease. After studying many literatures, this paper found that the existing heart disease prediction system has high requirements for clinical data. Based on the reality of the shortage of medical resources under the COVID-19 epidemic, this paper develops a simple heart disease prediction system, which predicts heart disease through simple and easy-to-measure data of patients, and then prevents heart disease. The method consists of two steps. First, collect the characteristics related to heart disease, and then select the most important 10 characteristics through correlation analysis and literature research, namely gender, age range, body mass index (BMI), smoking status, physical health index, walking difficulty, stroke status, skin cancer, diabetes, kidney disease. Second, an algorithm for heart disease based on artificial neural networks classification based on these features is developed. The prediction accuracy is close to 92%. In the future, the proposed model could be leveraged for heart disease recognition.

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Gasmi, Safa, Akila Djebbar, and Hayet Farida Merouani. "Enhancing Heart Disease Prediction using Deep Learning Model." All Sciences Abstracts 1, no. 2 (July 25, 2023): 5. http://dx.doi.org/10.59287/as-abstracts.1195.

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Cardiovascular disease, often known as heart disease, is the most common disease affecting people in general. Heart disease is a general term for any medical issue that impairs the heart's ability to pump blood. The most frequent cause of heart failure is a narrowing or blockage of the coronary arteries, which provide blood to the heart. The most frequent type of heart disease and the main factor in heart attacks is coronary artery disease (CAD). These days, there is no upper age restriction on who might contract this illness. There are many different ways for diagnosing diseases, but the majority are expensive, risky, and need technical professionals. Before a heart attack happens, effective treatment of cardiac patients depends on the accurate prediction of heart disease. Recently, a variety of machine learning techniques have been proposed to predict and diagnose cardiac diseases. However, these methods are unable to handle large data sets and as a result do not provide accurate results for the prediction of cardiovascular diseases. The purpose of this study is to assess a deep learning (DNN) model ability to predict the incidence of cardiovascular diseases using a widely used reference dataset.In comparison to other machine learning models, the deep learning model achieved the highest accuracy, F1-score, and precision values (97.07%, 97.07%, and 97.08%).

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