The dataset used is acath heart attack dataset provided by UCI machine learning repository. The results of the prediction give more accurate output than the other techniques.S.FlorenceN.G.Bhuvaneswari AmmaG.AnnapooraniK.MalathiResearch and ReviewsS.Florence1, Amma2, G.Annapoorani, K.Malathi, ...
We construct risk predictors using polygenic scores (PGS) computed from common Single Nucleotide Polymorphisms (SNPs) for a number of complex disease conditions, using L1-penalized regression (also known as LASSO) on case-control data from UK Biobank. Am
Heart Attack Risk Prediction Using Advanced Machine Learning Techniques Addressing the urgent global health challenge of cardiovascular diseases, particularly heart attacks, this research paper explores proactive healthcare sol... Yasaswini Bonthu,Subbarao Mannam,Gayithri Kandikunta,... - 2024 15th Inter...
The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper,
Fasting Blood Sugar: Not producing enough of a hormone secreted by your pancreas (insulin) or not responding to insulin properly causes your body’s blood sugar levels to rise, increasing your risk of a heart attack. Resting ECG: For people at low risk of cardiovascular disease, the USPSTF ...
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“We showed that risk prediction does not depend on cardiovascular risk scores, stenosis severity or CT calcium scoring,” the researchers write. “Rather, the risk of myocardial infarction [heart attack] is primarily governed by the analysis of plaque type and plaque burden provided by coronary ...
Validation of the prediction model is an essential step in machine learning processes. In this paper, the K-Fold cross-validation method is applied to validating the results of the above-mentioned classification models. K-fold cross validation (CV) In K-Fold CV, the whole dataset is split int...
In this work, reliable heart disease prediction system is implemented using strong Machine Learning algorithm which is the Random Forest algorithm. Which read patient record data set in the form of CSV file. After accessing dataset the operation is performed and effective heart attack level is ...
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