Heart Disease Prediction

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Multivariate Prediction of Coronary Heart Disease

The Western Collaborative Group Study (WCGS) is a prospective epidemiological study of 3,154 initially well men, aged 39-59 years at intake in 1960-61, who were employed in ten participating companies in California. Clinical coronary heart disease (CHD) occurred in 257 men during a follow-up period of eight and one-half years. Coronary heart disease risk is predicted using the additive multiple...

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Prediction of Heart Disease using Classification Algorithms

Data mining is an iterative progress in which evolution is defined by detection, through usual or manual methods. The discovered knowledge can be used for different applications for example healthcare industry. The heart disease accounts to be the leading cause of death worldwide. It is difficult for medical practitioners to predict the heart attack as it is complex task that requires experienc...

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Heart Disease Prediction Using Data Mining Techniques

There are huge amounts of data in the medical industry which is not processed properly and hence cannot be used effectively in making decisions. We can use data mining techniques to mine these patterns and relationships. This research has developed a prototype Heart Disease Prediction using data mining techniques, namely Neural Network, K-Means Clustering and Frequent Item Set Generation. Using...

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Genomic prediction of coronary heart disease

AIMS Genetics plays an important role in coronary heart disease (CHD) but the clinical utility of genomic risk scores (GRSs) relative to clinical risk scores, such as the Framingham Risk Score (FRS), is unclear. Our aim was to construct and externally validate a CHD GRS, in terms of lifetime CHD risk and relative to traditional clinical risk scores. METHODS AND RESULTS We generated a GRS of 4...

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ژورنال

عنوان ژورنال: International Journal of Engineering & Technology

سال: 2018

ISSN: 2227-524X

DOI: 10.14419/ijet.v7i3.12.16494