HEART DISEASE PREDICTION USING MACHINE LEARNING ALGORITHMS: A COMPARATIVE STUDY OF LOGISTIC REGRESSION AND KNN

Prashun Pareek; Nidhi Kumari; Pinky Yadav

doi:10.29121/granthaalayah.v13.i1.2025.6123

Authors

Prashun Pareek Department Of Computer Science & Engineering, Echelon Institute of Technology, Faridabad
Nidhi Kumari Department Of Computer Science & Engineering, Echelon Institute of Technology, Faridabad
Pinky Yadav Department Of Computer Science & Engineering, Echelon Institute of Technology, Faridabad

DOI:

https://doi.org/10.29121/granthaalayah.v13.i1.2025.6123

Keywords:

Heart Disease, Prediction, Machine Learning, Logistic, K-Nearest Neighbors (KNN)

Abstract [English]

This research presents a heart disease prediction system aimed at identifying individuals at risk based on their medical history. With the rising incidence of heart-related conditions, early diagnosis is essential for timely intervention. The system utilizes machine learning algorithms, specifically Logistic Regression and K-Nearest Neighbors (KNN), to classify patients as likely or unlikely to develop heart disease. Experimental results demonstrate improved prediction accuracy compared to traditional methods like Naïve Bayes. The proposed model not only enhances diagnostic precision but also contributes to cost-effective and efficient healthcare. The implementation is provided in .pynb format for practical usability.

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