Relevant bibliographies by topics / Early detection of heart disease

Academic literature on the topic 'Early detection of heart disease'

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Published: 12 May 2023

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Journal articles on the topic "Early detection of heart disease"

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Zabeeulla, M., C. Sharma, and A. Anand. "Early Detection of Heart Disease Using Machine Learning Approach." CARDIOMETRY, no. 26 (March 1, 2023): 342–47. http://dx.doi.org/10.18137/cardiometry.2023.26.342347.

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Heart attack is one of the leading causes of morbidity in the worldwide population. Cardiovascular disease is one of the major diseases involved in clinical data analysis or one of the most important part for forecasting. Early detection of cardiovascular diseases can help to reduce high-risk condition for heart patients to make individual decisions for their lifestyle adjustments, mitigating the challenges. Early detection of heart disease has been explored in this study using a machine-learning approach. Additionally, we used sampling strategies to deal with disparate datasets. The overall risk is estimated using a variety of machine-learning techniques. On Kaggle, the Heart Disease dataset is accessible and open for all. In present study testing set used this dataset. The ultimate objective is to determine whether the patient has a “10-year risk of developing coronary heart disease” (CHD). The dataset contained thirteen features that provided patient data, and the authors used machine learning algorithms to diagnose cardiac problems with 98.8% accuracy.
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Whalley, Gillian. "Appropriate and early detection of rheumatic heart disease." Australasian Journal of Ultrasound in Medicine 23, no. 1 (February 2020): 3–4. http://dx.doi.org/10.1002/ajum.12203.

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Anika and Navpreet Kaur. "A Review on Heart Disease Detection Techniques." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 7 (July 30, 2017): 395. http://dx.doi.org/10.23956/ijarcsse/v7i7/0200.

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The paper exhibits a formal audit on early detection of heart disease which are the major cause of death. Computational science has potential to detect disease in prior stages automatically. With this review paper we describe machine learning for disease detection. Machine learning is a method of data analysis that automates analytical model building.Various techniques develop to predict cardiac disease based on cases through MRI was developed. Automated classification using machine learning. Feature extraction method using Cell Profiler and GLCM. Cell Profiler a public domain software, freely available is flourished by the Broad Institute's Imaging Platform and Glcm is a statistical method of examining texture .Various techniques to detect cardio vascular diseases.
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Hanok Hruday Mohan, Yekula, Peddaguravagari Thejaswi, and Vanajakshamma. "School Health Screening For Early Detection Of Obesity, Congenital Heart Disease And Rheumatic Heart Disease." Indian Heart Journal 74 (November 2022): S10—S11. http://dx.doi.org/10.1016/j.ihj.2022.10.164.

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Kandukuri, Kumar, and A. Sandhya. "Heart Stroke Detection Using KNN Algorithm." ECS Transactions 107, no. 1 (April 24, 2022): 18385–93. http://dx.doi.org/10.1149/10701.18385ecst.

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Diagnosis of heart diseases have been improved in recent days with the help of machine learning (ML). The early prediction of heart disease is possible by analyzing the important parameters with the help of data mining techniques. In this study, K-Nearest Neighborhood (KNN) is used for the classification of heart stroke with parameter weighting methods to improve accuracy and 11 parameters were identified for training the KNN algorithm. The result shows that the accuracy using the KNN algorithm (11 parameters) is more efficient to predict the early heart stroke detection. This proposed algorithm accuracy is found to be better than the existing algorithms like Random Forest and Decision Tree and has an accuracy of average of 91.4%.
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Nagavelli, Umarani, Debabrata Samanta, and Partha Chakraborty. "Machine Learning Technology-Based Heart Disease Detection Models." Journal of Healthcare Engineering 2022 (February 27, 2022): 1–9. http://dx.doi.org/10.1155/2022/7351061.

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At present, a multifaceted clinical disease known as heart failure disease can affect a greater number of people in the world. In the early stages, to evaluate and diagnose the disease of heart failure, cardiac centers and hospitals are heavily based on ECG. The ECG can be considered as a regular tool. Heart disease early detection is a critical concern in healthcare services (HCS). This paper presents the different machine learning technologies based on heart disease detection brief analysis. Firstly, Naïve Bayes with a weighted approach is used for predicting heart disease. The second one, according to the features of frequency domain, time domain, and information theory, is automatic and analyze ischemic heart disease localization/detection. Two classifiers such as support vector machine (SVM) with XGBoost with the best performance are selected for the classification in this method. The third one is the heart failure automatic identification method by using an improved SVM based on the duality optimization scheme also analyzed. Finally, for a clinical decision support system (CDSS), an effective heart disease prediction model (HDPM) is used, which includes density-based spatial clustering of applications with noise (DBSCAN) for outlier detection and elimination, a hybrid synthetic minority over-sampling technique-edited nearest neighbor (SMOTE-ENN) for balancing the training data distribution, and XGBoost for heart disease prediction. Machine learning can be applied in the medical industry for disease diagnosis, detection, and prediction. The major purpose of this paper is to give clinicians a tool to help them diagnose heart problems early on. As a result, it will be easier to treat patients effectively and avoid serious repercussions. This study uses XGBoost to test alternative decision tree classification algorithms in the hopes of improving the accuracy of heart disease diagnosis. In terms of precision, accuracy, f1-measure, and recall as performance parameters above mentioned, four types of machine learning (ML) models are compared.
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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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Singh, Swati, Ankur Kaushal, Shashi Khare, and Ashok Kumar. "mga Genosensor for Early Detection of Human Rheumatic Heart Disease." Applied Biochemistry and Biotechnology 173, no. 1 (March 18, 2014): 228–38. http://dx.doi.org/10.1007/s12010-014-0836-z.

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Hara, Akira, Masayuki Niwa, Tomohiro Kanayama, Kei Noguchi, Ayumi Niwa, Mikiko Matsuo, Takahiro Kuroda, Yuichiro Hatano, Hideshi Okada, and Hiroyuki Tomita. "Galectin-3: A Potential Prognostic and Diagnostic Marker for Heart Disease and Detection of Early Stage Pathology." Biomolecules 10, no. 9 (September 4, 2020): 1277. http://dx.doi.org/10.3390/biom10091277.

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The use of molecular biomarkers for the early detection of heart disease, before their onset of symptoms, is an attractive novel approach. Ideal molecular biomarkers, those that are both sensitive and specific to heart disease, are likely to provide a much earlier diagnosis, thereby providing better treatment outcomes. Galectin-3 is expressed by various immune cells, including mast cells, histiocytes and macrophages, and plays an important role in diverse physiological functions. Since galectin-3 is readily expressed on the cell surface, and is readily secreted by injured and inflammatory cells, it has been suggested that cardiac galectin-3 could be a marker for cardiac disorders such as cardiac inflammation and fibrosis, depending on the specific pathogenesis. Thus, galectin-3 may be a novel candidate biomarker for the diagnosis, analysis and prognosis of various cardiac diseases, including heart failure. The goals of heart disease treatment are to prevent acute onset and to predict their occurrence by using the ideal molecular biomarkers. In this review, we discuss and summarize recent developments of galectin-3 as a next-generation molecular biomarker of heart disease. Furthermore, we describe how galectin-3 may be useful as a diagnostic marker for detecting the early stages of various heart diseases, which may contribute to improved early therapeutic interventions.
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Catur Andryani, S.Si., MSc., Dr Nur Afny, Muhamad Femy Mulya, Surnanto Surnanto, and M. Rizam Kusfandi. "Rancang Bangun Purwarupa Aplikasi Deteksi Dini Penyakit Jantung Berbasis Case Base Reasoning dengan Keamanan Data." Jurnal SISKOM-KB (Sistem Komputer dan Kecerdasan Buatan) 5, no. 1 (September 30, 2021): 66–73. http://dx.doi.org/10.47970/siskom-kb.v5i1.230.

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The prevalence of heart disease has been consistently increasing in five recent years. In average 15 out of 1000 people have heart disease. Currently heart disease becomes the second leading cause of death in Indonesia. Early detection will guide the appropriate treatments to increase recovery opportunity. In another hand, many healthcare facilities in Indonesia are not equipped with the cardiologist. It triggers many heart disease cases are late to handle due late detection. Thus, we propose web based early heart disease detection application prototype using Case Base Reasoning framework. It is intended to support small clinic or other healthcare facilities which have no cardiologist to provide early detection of heart disease. The application is equipped with data security to handle the data privacy of the patient. Based on the black box evaluation by the expert, it is concluded that all the provided features can be run functionally.
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Dissertations / Theses on the topic "Early detection of heart disease"

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Hartnick, Maria Diana. "Echocardiography for early detection of heart disease in high risk diabetic patients." Thesis, Cape Peninsula University of Technology, 2015. http://hdl.handle.net/20.500.11838/1566.

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Masters of Technology: Radiography in the Faculty of Health and Wellness Sciences at the Cape Peninsula University of Technology 2015
Introduction: Diabetes mellitus is a chronic disease with a significant impact on personal lifestyle and wellbeing. It is associated with a high prevalence of myocardial disease, the early detection of which is important for prevention of disease progression. Although echocardiography is recognised as a leading cardiovascular imaging modality, there has been limited work on its role in the early detection of diabetes-related myocardial dysfunction. The aim of this study was therefore to evaluate the role of echocardiography in the early detection of diabetes-related myocardial disease, in a population with a high prevalence of type 2 diabetes mellitus. Methodology: A single sonographer, blinded to individual biochemical markers conducted detailed echocardiographic examinations on 407 participants from a Cape Town community with a high prevalence of diabetes mellitus. Participants were subsequently stratified by biochemical status, as normoglyceamia or hyperglycaemia. The echocardiographic features of the two groups were compared using the Pearson chi-squared and Mann-Whitney U tests. Findings: Hyperglycaemia was associated with left atrium (LA) enlargement (p ˂ 0.0014), aortic enlargement (p ˂ 0.0067) and inter-ventricular septal (IVS) thickening (p ˂ 0.0001). Conclusion: The findings suggest that echocardiography can be a useful screening tool for myocardial dysfunction in Type 2 diabetes mellitus.
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Kounali, Daphne. "Early growth and coronary heart disease." Thesis, University of Southampton, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.436926.

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Springer, David Brian. "Mobile phone-based rheumatic heart disease detection." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:5ec8c818-dafb-4571-8198-97607f8d0451.

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Rheumatic heart disease (RHD), the permanent damage of the heart valves caused by an untreated 'strep throat' infection, is the leading cause of cardiovascular mortality and morbidity in children and young adults worldwide. Simple penicillin treatment after the early diagnosis of RHD can stop recurring bouts of the condition, which lead to the most severe valvulopathy, and ultimately, heart failure. However, RHD is an under-diagnosed condition in the developing world, as such a diagnosis requires, at a minimum, a trained clinician to perform auscultation to detect pathological heart sounds. Trained medical personnel are scarce in the countries where RHD is most prevalent. A low-cost, mobile phone-based automatic diagnostic tool offers a potential solution, allowing a non-medically trained individual to screen for RHD in those countries. An essential feature of such a device is feedback on the signal quality of heart sound recordings. The first major contribution of this thesis is the investigation of features and algorithms for the automatic signal quality assessment of heart sound recordings. These algorithms are able to differentiate between good- and poor-quality recordings in over 80% of cases when using both a low-cost mobile phone-based stethoscope and an electronic stethoscope. Once the quality of recordings is ensured, the positions of the first and second heart sounds need to be located in a process called segmentation. This thesis extends the state-of-the art hidden semi-Markov models by: investigating additional features; extending the Viterbi algorithm; incorporating logistic regression into the model to form a hybrid generative-discriminative model; and investigating a discriminative duration-dependent probabilistic model - a conditional random field. These extensions are found to outperform the state-of-the-art method. Lastly, the period between the first and second heart sounds can be analysed for the presence of a pathological murmur. This thesis presents automated systolic murmur classification algorithms based on wavelet and mel-frequency cepstral coefficient-based features along with denoising via cycle averaging. These algorithms outperform three methods from the literature when detecting valvulopathy, while also outperforming a cardiologist and commercial software when detecting RHD in mobile phone-based heart sound recordings.
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Bull, Adrian Richard. "Early determinants of blood pressure and related disease." Thesis, University of Southampton, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.238962.

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Suh, Doug Young. "Knowledge-based boundary detection system : on MRI cardiac image sequences." Diss., Georgia Institute of Technology, 1990. http://hdl.handle.net/1853/13291.

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Pursiainen, V. (Ville). "Autonomic dysfunction in early and advanced Parkinson's disease." Doctoral thesis, University of Oulu, 2007. http://urn.fi/urn:isbn:9789514283888.

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Abstract Parkinson's disease (PD) is known to affect both the extrapyramidal system and the autonomic nervous system even in the early phases of the disease. This study was designed to evaluate cardiovascular autonomic regulation in early PD by measuring heart rate (HR) variability from 24-hour ECG recordings. The dynamics of blood pressure (BP), HR and sweating in patients with and without wearing-off were assessed during clinical observations after a morning dose of levodopa. In patients with wearing-off the tests were repeated after selegiline withdrawal. The power spectral components of HR variability and the SD1 value of the Poincaré analysis that quantifies the short-term beat-to-beat variability were suppressed at night in the PD patients. During the daytime only the SD1 of the Poincaré was suppressed. The results indicate impairment of parasympathetic cardiovascular regulation in untreated patients with PD. The dysfunction was more pronounced at night and in patients with more severe PD. The patients with wearing-off had fluctuation of BP during the observation period, BP increasing when the motor performance worsened and vice versa (p < 0.001). The patients without wearing-off did not show fluctuation of BP. Sweating increased during the observation period, and reached its maximum level at the time of the highest UPDRS motor score phase (off-stage) in patients with wearing-off, but in the patients without wearing-off no changes in sweating were observed. Sweating of the hands was significantly higher in PD patients with motor fluctuations than in those without. Selegiline withdrawal decreased systolic BP significantly during the on-stage in a supine position as well as during the orthostatic test. The initial drop of BP in the orthostatic test was significantly smaller after selegiline withdrawal. The HR and sweating remained unaffected. The results show that the autonomic nervous system is affected in the early phases of PD. The dysfunction becomes more pronounced with the disease progression. Wearing-off type motor fluctuations are associated with fluctuation of BP and sweating and these fluctuations may represent autonomic dysfunction caused by PD, the effect of PD medication, or both. Selegiline withdrawal seems to alleviate the orthostatic reaction in patients with advanced PD.
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Shen, Ze-ping. "An application of neural networks for the detection of coronary heart disease." Thesis, Brunel University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.385186.

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Heard, Stephanie. "Plant pathogen sensing for early disease control." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/plant-pathogen-sensing-for-early-disease-control(48949f80-2596-4ce2-912a-6513e72f6a8d).html.

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Sclerotinia sclerotiorum, a fungal pathogen of over 400 plant species has been estimated to cost UK based farmers approximately £20 million per year during severe outbreak (Oerke and Dehne 2004). S. sclerotiorum disease incidence is difficult to predict as outbreaks are often sporadic. Ascospores released from the fruiting bodies or apothecia can be dispersed for tens of kilometres. This makes disease control problematic and with no S. sclerotiorum resistant varieties available, growers are forced to spray fungicides up to three times per flowering season in anticipation of the arrival of this devastating disease. This thesis reports the development of the first infield S. sclerotiorum biosensor which aims to enable rapid detection of airborne ascospores, promoting a more accurate disease risk assessment and fungicide spraying regime. The sensor is designed to detect the presence of oxalic acid, the main pathogenicity factor secreted during early S. sclerotiorum ascospore germination. Upon electrochemical detection of this analyte in the biosensor, a binary output is relayed to farmer to warm him of a disease risk. This project focused on the development of a nutrient matrix which was designed to be contained within the biosensor. The role of this matrix was to promote the growth of captured airborne S. sclerotiorum ascospores and induce high levels of oxalic acid secretion. The use of the designed biological matrix to promote oxalic acid production was tested during three field trials in S. sclerotiorum artificially inoculated fields. This thesis describes the use of contemporary pathogenomics technologies to further investigate candidate genes involved in pathogenicity alongside the secretion of oxalic acid. A pre-described bioinformatics pipeline was used to predict the S. sclerotiorum secretome to identify potential effector proteins as well as explore proteins which are unique to S. sclerotiorum to be used as other novel targets for detection. GFP tagged constructs were designed to investigate the expression of the putative targets for S. sclerotiorum detection. The transcriptomes of wild type and oxalic acid deficient S. sclerotiorum strains during infection as well as during a saprotrophic stage were investigated. This study provided expression support for not only some of the unannotated genes identified in the putative secretome, but some candidate genes speculated to be involved in infection.
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Mendel, Julian L. "Laurel Wilt Disease: Early Detection through Canine Olfaction and "Omics" Insights into Disease Progression." FIU Digital Commons, 2017. http://digitalcommons.fiu.edu/etd/3475.

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Laurel wilt disease is a vascular wilt affecting the xylem and water conductivity in trees belonging to the family Lauraceae. The disease was introduced by an invasive species of ambrosia beetle, Xyleborus glabratus. The beetle, together with its newly described fungal symbiont Raffaelea lauricola (pathogenic to host trees), has lead to the devastation and destruction of over 300 million wild redbay trees in southeastern forests. Ambrosia beetles make up a very unique clade of beetle and share a co-evolved obligatory mutualistic relationship with their partner fungi. Rather than consuming host tree material, the beetles excavate galleries or canals within them. These galleries serve two purposes: reproduction and fungal gardening. The beetles house fungal spores within specialized sacs, mycangia, and essentially inoculate host trees with the pathogenic agent. They actively grow and cultivate gardens of the fungus in galleries to serve as their sole food source. Once the fungus reaches the xylem vessels of the host tree, it thrives and leads to the blockage of water flow, both because of fungal accumulation and to the host response of secreting gels, gums and tyloses to occlude vessels in an attempt to quarantine the fungus. This disease spreads rapidly, and as a result, once symptoms become visible to the naked eye, it is already too late to save the tree, and it has likely already spread to adjacent ones. The present study presents the first documented study involving the early detection of disease from deep within a tree through the use of scent-discriminating canines. In addition, the present study has lead to the development of a novel sample collection device enabling the non-destructive sampling of beetle galleries. Finally, a metabolomics approach revealed key biochemical pathway modifications in the disease state, as well as potential clues to disease development.
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Balderson, Diane E. "Observations on the detection of ventricular late potentials." Thesis, Queen's University Belfast, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.238982.

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Books on the topic "Early detection of heart disease"

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Carlos, Kaski Juan, and Holt David W, eds. Myocardial damage: Early detection by novel biochemical markers. Dordrecht: Kluwer Academic, 1998.

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Policy, Toronto Working Group on Cholesterol. Detection and management of asymptomatic hypercholesterolemia: A policy document. [Toronto?: s.n., 1989.

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Shen, Ze-ping. An application of neural networks for the detection of coronary heart disease. Uxbridge: Brunel University, 1994.

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McCarthy, Joseph C. Early hip disorders: Advances in detection and minimally invasive treatment. New York: Springer, 2011.

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editor, Mordini E. (Emilio), and Green Manfred editor, eds. Internet-based intelligence in public health emergencies: Early detection and response in disease outbreak crises. Amsterdam, Netherlands: IOS Press, 2011.

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name, No. Early hip disorders: Advances in detection and minimally invasive treatment. New York, NY: Springer, 2003.

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Report of the Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Bethesda, Md.?]: U.S. Department of Health and Human Services, Public Health Service, National Institutes of Health, National Cholesterol Education Program, 1989.

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Rosen, Shara. Trends in the early diagnosis of cardiovascular disease: Worldwide market opportunities. New York: Kalorama Information, 2001.

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Fitzgerald, Rebecca C. Pre-invasive disease: Pathogenesis and clinical management. New York: Springer, 2011.

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Patlak, Margie. Mammography and beyond: Developing technologies for the early detection of breast cancer : a non-technical summary. Edited by National Cancer Policy Board (U.S.). Committee on the Early Detection of Breast Cancer and National Research Council (U.S.). Commission on Life Sciences. Washington, D.C: National Academy Press, 2001.

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Book chapters on the topic "Early detection of heart disease"

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Forbes, Malcolm P., and Harris A. Eyre. "Screening for Depression in Coronary Heart Disease: Detection of Early Disease States." In Cardiovascular Diseases and Depression, 519–33. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32480-7_28.

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Mishra, Sarita, Manjusha Pandey, Siddharth Swarup Rautaray, and Mahendra Kumar Gourisaria. "A Proposal for Early Detection of Heart Disease Using a Classification Model." In Communications in Computer and Information Science, 360–67. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1480-4_32.

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Feightner, John, and Graham Worrall. "Early Detection of Depression." In Preventing Disease, 118–28. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4612-3280-3_15.

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Wallace, Deborah, and Rodrick Wallace. "Early Mortality from Ischemic Heart Disease (Coronary Heart Disease)." In Right-to-Work Laws and the Crumbling of American Public Health, 61–69. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72784-4_6.

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Macakaite, Karina, and Arjab Singh Khuman. "Risk Detection of Heart Disease." In Artificial Intelligence in Healthcare, 261–74. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5272-2_14.

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Postema, Pieter G. "Idiopathic Ventricular Fibrillation and Early Repolarization." In Channelopathies in Heart Disease, 257–75. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77812-9_11.

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Mattioli, Veronica. "Vital Signs: Parameters, Frequency, and Pediatric and Cardiac Early Warning Scores." In Congenital Heart Disease, 81–95. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78423-6_3.

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Duprez, Daniel A., and Jay N. Cohn. "Detection of Early Cardiovascular Disease." In Cardiovascular Medicine, 1615–22. London: Springer London, 2007. http://dx.doi.org/10.1007/978-1-84628-715-2_78.

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Carbonetto, Claudia A. P., Renaldo N. Battista, and Jeannie Haggerty. "Early Detection and Counseling of Problem Drinkers." In Preventing Disease, 84–91. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4612-3280-3_10.

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Mittnacht, Alexander. "Early Tracheal Extubation and Postoperative Pain Management." In Anesthesia for Congenital Heart Disease, 451–67. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2015. http://dx.doi.org/10.1002/9781118768341.ch20.

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Conference papers on the topic "Early detection of heart disease"

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Dubey, Shivansh, Raghuvendra Pratap Tripathi, Malay Kishore Dutta, Jan Dorazil, and Petr Kriz. "Early Detection of Heart Valve Disease Employing Multiclass Classifier." In 2019 11th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT). IEEE, 2019. http://dx.doi.org/10.1109/icumt48472.2019.8970870.

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Deepika, R., P. Balaji Srikaanth, and R. Pitchai. "Early Detection of Heart Disease Using Deep Learning Model." In 2022 8th International Conference on Smart Structures and Systems (ICSSS). IEEE, 2022. http://dx.doi.org/10.1109/icsss54381.2022.9782295.

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Gemke, "Philip, Nicolai Spicher, and Tim Kacprowski." "An LSTM-based Listener for Early Detection of Heart Disease." In 2022 Computing in Cardiology Conference. Computing in Cardiology, 2022. http://dx.doi.org/10.22489/cinc.2022.151.

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Zennifa, Fadilla, Fitrilina, Husnil Kamil, and Keiji Iramina. "Prototype early warning system for heart disease detection using Android application." In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2014. http://dx.doi.org/10.1109/embc.2014.6944369.

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Kumar Trivedi, Naresh, Shikha Maheswari, Himanshu Sharma, Sachin Jain, and Sumit Agarwal. "Early Detection & Prediction of Heart Disease using Various Machine Learning Approaches." In 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, 2022. http://dx.doi.org/10.23919/indiacom54597.2022.9763188.

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Shishah, Wesam. "An Efficient Early Stage Heart Disease Risk Detection Using Machine Learning Techniques." In 2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T). IEEE, 2022. http://dx.doi.org/10.1109/icpc2t53885.2022.9777070.

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Mahmood, Ali Mirza, and Mrithyumjaya Rao Kuppa. "Early Detection of Clinical Parameters in Heart Disease by Improved Decision Tree Algorithm." In 2010 Second Vaagdevi International Conference on Information Technology for Real World Problems (VCON). IEEE, 2010. http://dx.doi.org/10.1109/vcon.2010.12.

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Mehta, Dhara B., and Nirali C. Varnagar. "Newfangled Approach for Early Detection and Prevention of Ischemic Heart Disease using Data Mining." In 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2019. http://dx.doi.org/10.1109/icoei.2019.8862544.

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Ed-Daoudy, Abderrahmane, and Khalil Maalmi. "Real-time machine learning for early detection of heart disease using big data approach." In 2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS). IEEE, 2019. http://dx.doi.org/10.1109/wits.2019.8723839.

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Gupta, Priyanka, and D. D. Seth. "Comparative Analysis of Machine Learning Classifiers for Accurate and Early Detection of Heart Disease." In 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2022. http://dx.doi.org/10.1109/icrito56286.2022.9964882.

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Reports on the topic "Early detection of heart disease"

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Wu, Meiye, Ryan Wesley Davis, and Anson Hatch. Portable microfluidic raman system for rapid, label-free early disease signature detection. Office of Scientific and Technical Information (OSTI), September 2015. http://dx.doi.org/10.2172/1222536.

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Rostaminejad, Marzieh. Early Diagnosis of Alzheimer's disease using Electrochemical-based Nanobiosensors for miRNA Detection. Peeref, July 2022. http://dx.doi.org/10.54985/peeref.2207p6024343.

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Deshpande, Alina. RED Alert – Early warning or detection of global re-emerging infectious disease (RED). Office of Scientific and Technical Information (OSTI), July 2016. http://dx.doi.org/10.2172/1261795.

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Tang, Xiangyang. Early Detection of Amyloid Plaque in Alzheimer's Disease via X-Ray Phase CT. Fort Belvoir, VA: Defense Technical Information Center, June 2014. http://dx.doi.org/10.21236/ada612057.

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Tang, Xiangyang. Early Detection of Amyloid Plaque in Alzheimer's Disease via X-Ray Phase CT. Fort Belvoir, VA: Defense Technical Information Center, June 2013. http://dx.doi.org/10.21236/ada582946.

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Tang, Xiangyang. Early Detection of Amyloid Plaque in Alzheimer's Disease Via X-ray Phase CT. Fort Belvoir, VA: Defense Technical Information Center, June 2015. http://dx.doi.org/10.21236/ada620373.

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Li, Jiangwei. Applications of a single-molecule detection in early disease diagnosis and enzymatic reaction study. Office of Scientific and Technical Information (OSTI), January 2008. http://dx.doi.org/10.2172/964365.

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Cohen, Yuval, Christopher A. Cullis, and Uri Lavi. Molecular Analyses of Soma-clonal Variation in Date Palm and Banana for Early Identification and Control of Off-types Generation. United States Department of Agriculture, October 2010. http://dx.doi.org/10.32747/2010.7592124.bard.

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Date palm (Phoenix dactylifera L.) is the major fruit tree grown in arid areas in the Middle East and North Africa. In the last century, dates were introduced to new regions including the USA. Date palms are traditionally propagated through offshoots. Expansion of modern date palm groves led to the development of Tissue Culture propagation methods that generate a large number of homogenous plants, have no seasonal effect on plant source and provide tools to fight the expansion of date pests and diseases. The disadvantage of this procedure is the occurrence of off-type trees which differ from the original cultivar. In the present project we focused on two of the most common date palm off-types: (1) trees with reduced fruit setting, in which most of the flowers turn into three-carpel parthenocarpic fruits. In a severe form, multi-carpel flowers and fruitlets (with up to six or eight carpels instead of the normal three-carpel flowers) are also formed. (2) dwarf trees, having fewer and shorter leaves, very short trunk and are not bearing fruits at their expected age, compared to the normal trees. Similar off-types occur in other crop species propagated by tissue culture, like banana (mainly dwarf plants) or oil palm (with a common 'Mantled' phenotype with reduced fruit setting and occurrence of supernumerary carpels). Some off-types can only be detected several years after planting in the fields. Therefore, efficient methods for prevention of the generation of off-types, as well as methods for their detection and early removal, are required for date palms, as well as for other tissue culture propagated crops. This research is aimed at the understanding of the mechanisms by which off-types are generated, and developing markers for their early identification. Several molecular and genomic approaches were applied. Using Methylation Sensitive AFLP and bisulfite sequencing, we detected changes in DNA methylation patterns occurring in off-types. We isolated and compared the sequence and expression of candidate genes, genes related to vegetative growth and dwarfism and genes related to flower development. While no sequence variation were detected, changes in gene expression, associated with the severity of the "fruit set" phenotype were detected in two genes - PdDEF (Ortholog of rice SPW1, and AP3 B type MADS box gene), and PdDIF (a defensin gene, highly homologous to the oil palm gene EGAD). We applied transcriptomic analyses, using high throughput sequencing, to identify genes differentially expressed in the "palm heart" (the apical meristem and the region of embryonic leaves) of dwarf vs. normal trees. Among the differentially expressed genes we identified genes related to hormonal biosynthesis, perception and regulation, genes related to cell expansion, and genes related to DNA methylation. Using Representation Difference Analyses, we detected changes in the genomes of off-type trees, mainly chloroplast-derived sequences that were incorporated in the nuclear genome and sequences of transposable elements. Sequences previously identified as differing between normal and off-type trees of oil palms or banana, successfully identified variation among date palm off-types, suggesting that these represent highly labile regions of monocot genomes. The data indicate that the date palm genome, similarly to genomes of other monocot crops as oil palm and banana, is quite unstable when cells pass through a cycle of tissue culture and regeneration. Changes in DNA sequences, translocation of DNA fragments and alteration of methylation patterns occur. Consequently, patterns of gene expression are changed, resulting in abnormal phenotypes. The data can be useful for future development of tools for early identification of off-type as well as for better understanding the phenomenon of somaclonal variation during propagation in vitro.
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Gafny, Ron, A. L. N. Rao, and Edna Tanne. Etiology of the Rugose Wood Disease of Grapevine and Molecular Study of the Associated Trichoviruses. United States Department of Agriculture, September 2000. http://dx.doi.org/10.32747/2000.7575269.bard.

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Rugose wood is a complex disease of grapevines, characterized by modification of the woody cylinder of affected vines. The control of rugose wood is based on the production of healthy propagation material. Detection of rugose wood in grapevines is difficult and expensive: budwood from tested plants is grafted onto sensitive Vitis indicators and the appearance of symptoms is monitored for 3 years. The etiology of rugose wood is complex and has not yet been elucidated. Several elongated clostero-like viruses are consistently found in affected vines; one of them, grapevine virus A (GVA), is closely associated with Kober stem grooving, a component of the rugose wood complex. GVA has a single-stranded RNA genome of 7349 nucleotides, excluding a polyA tail at the 3' terminus. The GVA genome includes five open reading frames (ORFs 1-5). ORF 4, which encodes for the coat protein of GVA, is the only ORF for which the function was determined experimentally. The original objectives of this research were: 1- To produce antisera to the structural and non-structural proteins of GVA and GVB and to use these antibodies to establish an effective detection method. 2- Develop full length infectious cDNA clones of GVA and GVB. 3- Study the roll of GVA and GVB in the etiology of the grapevine rugose wood disease. 4- Determine the function of Trichovirus (now called Vitivirus) encoded genes in the virus life cycle. Each of the ORFs 2, 3, 4 and 5 genes of GVA were cloned and expressed in E. coli and used to produce antisera. Both the CP (ORF 4) and the putative MP (ORF 3) were detected with their corresponding antisera in-GVA infected N. benthamiana and grapevine. The MP was first detected at an early stage of the infection, 6-12 h after inoculation, and the CP 2-3 days after inoculation. The MP could be detected in GVA-infected grapevines that tested negative for CP, both with CP antiserum and with a commercially available ELISA kit. Antisera to ORF 2 and 5 encoded proteins could react with the recombinant proteins but failed to detect both proteins in GVA infected plants. A full-length cDNA clone of grapevine virus A (GVA) was constructed downstream from the bacteriophage T7 RNA polymerase promoter. Capped in vitro transcribed RNA was infectious in N. benthamiana and N. clevelandii plants. Symptoms induced by the RNA transcripts or by the parental virus were indistinguishable. The infectivity of the in vitro-transcribed RNA was confirmed by serological detection of the virus coat and movement proteins and by observation of virions by electron microscopy. The full-length clone was modified to include a gus reporter gene and gus activity was detected in inoculated and systemic leaves of infected plants. Studies of GVA mutants suggests that the coat protein (ORF 4) is essential for cell to cell movement, the putative movement protein (ORF 3) indeed functions as a movement protein and that ORF 2 is not required for virus replication, cell to cell or systemic movement. Attempts to infect grapevines by in-vitro transcripts, by inoculation of cDNA construct in which the virus is derived by the CaMV 35S promoter or by approach grafting with infected N. benthamiana, have so far failed. Studies of the subcellular distribution of GFP fusion with each of ORF 2, 3 and 4 encoded protein showed that the CP fusion protein accumulated as a soluble cytoplasmatic protein. The ORF 2 fusion protein accumulated in cytoplasmatic aggregates. The MP-GFP fusion protein accumulated in a large number of small aggregates in the cytoplasm and could not move from cell to cell. However, in conditions that allowed movement of the fusion protein from cell to cell (expression by a PVX vector or in young immature leaves) the protein did not form cytoplasmatic aggregates but accumulated in the plasmodesmata.
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Ciapponi, Agustín. What is the effectiveness of interventions targeted at women to improve the uptake of cervical cancer screening? SUPPORT, 2016. http://dx.doi.org/10.30846/1611112.

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World-wide, cervical cancer is the second most common cancer in women and more than 85% of women dying from cervical cancer live in the developing world. Increasing the uptake of screening, alongside increasing informed choice, is key to controlling this disease through prevention and early detection. Methods of encouraging women to undergo cervical screening include invitations to screening; reminders to attend screening; education to increase knowledge of screening programmes or of cervical cancer; message framing (positive or negative messages about screening); counselling regarding barriers to screening; risk factor assessment of individuals; procedures, such as making the screening process easier; and economic interventions, such as incentives to attend screening.
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