Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3732
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dc.contributor.authorSahoo, Goutam Kumar-
dc.contributor.authorKanike, Keerthana-
dc.contributor.authorDas, Santos Kumar-
dc.contributor.authorSingh, Poonam-
dc.date.accessioned2022-09-05T07:19:42Z-
dc.date.available2022-09-05T07:19:42Z-
dc.date.issued2022-08-
dc.identifier.citationIEEE International Workshop on Machine Learning for Signal Processing, Aug. 22--25, 2022, Xi'an, Chinaen_US
dc.identifier.urihttp://hdl.handle.net/2080/3732-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractThis study develops a framework for personalized care to tackle heart disease risk using an at-home system. The machine learning models used to predict heart disease are Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Naive Bayes, Decision Tree, Random Forest, and XG Boost. Timely and efficient detection of heart disease plays an important role in health care. It is essential to detect cardiovascular disease (CVD) at the earliest, consult a specialist doctor before the severity of the disease and start medication. The performance of the proposed model was assessed using the Cleveland Heart Disease dataset from the UCI Machine Learning Repository. Compared to all machine learning algorithms, the Random Forest algorithm shows a better performance accuracy score of 90.16%. The best model may evaluate patient fitness rather than routine hospital visits. The proposed work will reduce the burden on hospitals and help hospitals reach only critical patients.en_US
dc.subjectheart diseaseen_US
dc.subjectLogistic Regressionen_US
dc.subjectK-Nearest Neighboren_US
dc.titleMachine Learning-Based Heart Disease Prediction: A Study for Home Personalized Careen_US
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