Heart Attack Prediction using KNN, Naive Bayes and decision tree

Abstract

Title: Enhanced Heart Attack Prediction Using KNN, Naive Bayes, and Decision Tree Models Description: This research paper presents a comparative analysis of three fundamental machine learning algorithms—K-Nearest Neighbors (KNN), Naive Bayes, and Decision Tree—for the purpose of predicting the likelihood of heart attacks using real-world cardiovascular data. The dataset includes over 70,000 patient records with features such as age, gender, blood pressure, cholesterol, glucose levels, and BMI. The research follows a complete machine learning pipeline, including data cleaning, feature encoding, normalization, correlation analysis, and model training and evaluation. The performance of the models was measured using accuracy, precision, recall, and F1-score. Visual tools like heatmaps, confusion matrices, and feature importance plots were used to interpret and compare the models effectively. The findings indicate that the Decision Tree classifier outperformed the others in terms of accuracy and interpretability, while Naive Bayes proved computationally efficient. KNN performed well with parameter tuning via GridSearchCV. This study highlights the importance of model selection in healthcare analytics and proposes future work involving real-time dashboards and expanded datasets. Authors: Shan Khanna, Karthi K. Affiliation: Department of MCA, Chanakya University, Bengaluru, Karnataka, India Keywords: Heart attack prediction, Machine Learning, KNN, Naive Bayes, Decision Tree, Cardiovascular Disease, Medical Data, Health Analytics