RESEARCH ON MACHINE LEARNING ALGORITHM ON HEART DISEASE PREDICTION

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Shashwat Srivastava; Sayyed Zamin Abbas; Harsh Kesharwani

RESEARCH ON MACHINE LEARNING ALGORITHM ON HEART DISEASE PREDICTION

Authors: Shashwat Srivastava, Sayyed Zamin Abbas, Harsh Kesharwani

Unique Paper ID: 178063
Volume: 11
Issue: 12
PageNo: 3302-3308

Keywords: No Keywords Found

Abstract:
Preventative medical intervention depends on early heart attack identification because heart attacks represent the globe's most detrimental group of mortality causes. Heart attack predictions now become possible thanks to machine learning (ML) improvements that develop precise prediction models. The research explores and breaks down three key investigations regarding the use of machine learning for heart attack prediction. The study uses Decision Tree (DT) Random Forest (RF) and Support Vector Machine (SVM) and Gradient Boosting to find the most suitable machine learning algorithm. The research indicates that Support Vector Machines (SVM) functions effectively for healthcare prediction while demonstrating 91.67% accuracy as its top performance. Heart disease detection and support vector machines and random forest together with machine learning represent the main discussion points in this analysis.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 3302-3308

RESEARCH ON MACHINE LEARNING ALGORITHM ON HEART DISEASE PREDICTION

Available:https://ijirt.org/article?manuscript=178063

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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