Heart Disease Prediction Using Multiple Machine Learning Algorithms

Authors: Pradip M Paithane* and Atharva J

DOI: 10.23880/art-16000114

Volume 2, Issue 1

Abstract

Heart disease is a significant health concern globally, and the ability to predict and diagnose it accurately is crucial for effective treatment and prevention strategies. Machine learning algorithms have shown promise in enhancing the prediction of heart disease by analysing complex medical data. So in this paper, we have analysed and compared different machine learning algorithms like Logistic Regression, SVM and Naive Bayes(Gaussian Naive Bayes) for the prediction of heart disease. In proposed work the data used consist of different medical attributes like age, heart rate, chest pain type, restingBP, max heart rate, etc. To increase the accuracy of the models I used cross validation technique (Kfold). The Support vector machine received highest accuracy as compared to other approaches.

Keywords: Machine Learning; Logistic Regression; Naive Bayes; SVM; Data Preprocessing; Healthcare Analytics; Heart Disease

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