Download PDFOpen PDF in browserCardiovascular Disease Prediction Using Machine LearingEasyChair Preprint 126495 pages•Date: March 21, 2024AbstractCardiovascular disease (CVD) remains a significant cause of mortality globally, with high prevalence rates in countries like India. Early detection and accurate prediction of CVD are crucial for timely intervention and treatment. In this study, we employ various machine learning techniques to analyze a dataset containing multiple factors associated with heart disease. Data preprocessing, exploratory data analysis, feature correlation analysis, and model building are performed to predict the occurrence of heart disease. Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Decision Trees (DT), Logistic Regression (LR), and Random Forest (RF) algorithms are evaluated for their predictive performance. The results provide insights into the effectiveness of different machine learning approaches in detecting cardiovascular disease. This paper investigates that which technique gives more accuracy in predicting heart disease based on health parameters. Experiment show that Naïve Bayes has the highest accuracy of 88%. Keyphrases: K-Nearest Neighbor, Random Forest, Support Vector Machines, decision trees, logistic regression
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