Comprehensive Analysis of Machine Learning Models for Cardiovascular Disease Detection and Diagnosis

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D P Singh

Abstract

Cardiovascular disease (CVD) is a leading cause of mortality globally, making early detection and diagnosis crucial for improving patient outcomes and reducing healthcare costs. Machine learning (ML) models offer promising capabilities for predicting the likelihood of cardiovascular disease, thus assisting in timely diagnosis and treatment. This study conducts an extensive analysis of various ML models, including decision trees, logistic regression, support vector machines, and ensemble methods, to evaluate their effectiveness in predicting cardiovascular diseases. Performance metrics such as accuracy, precision, recall, F1 score, and cross-validation accuracy are utilized to evaluate and compare the effectiveness of models. The findings highlight the potential of machine learning (ML) to improve early prediction and diagnosis of cardiovascular diseases. Through the comparison and analysis of the applied algorithms on the Cleveland and Stat log heart datasets, this research furthers the development of ML techniques in healthcare. The developed machine learning system acts as a valuable resource for healthcare professionals, aiding in the early diagnosis and prediction of cardiovascular diseases, while also offering potential applications for identifying and diagnosing other medical conditions.

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