• DR CHETHAN CHANDRA S BASAVARADDI
• Dr. G Vasanth

Heart disease continues to be one of the major causes of mortality across the globe, emphasizing the need for accurate, efficient, and early diagnostic systems. The exponential growth of healthcare data has paved the way for machine learning (ML) techniques to play a significant role in disease prediction and decision support systems. This paper presents an enhanced and comprehensive analysis of multiple machine learning algorithms for heart disease prediction, including Decision Tree, Naïve Bayes, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest. A structured methodology involving data preprocessing, feature selection, model training, and performance evaluation has been employed on a clinical dataset containing demographic and medical attributes. Performance is evaluated using accuracy, precision, recall, F1-score, and ROC analysis. Experimental results demonstrate that Random Forest achieves superior performance, making it a reliable choice for clinical decision support. The proposed system highlights the potential of machine learning in improving healthcare outcomes and reducing diagnostic errors.

Heart Disease Prediction, Machine Learning, Healthcare Analytics, Data Mining, Clinical Decision Support Systems

"HEART DISEASE ANALYSIS USING MACHINE LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 1, page no.a831-a834, January-2026, Available :http://www.jetir.org/papers/JETIR2601098.pdf

"HEART DISEASE ANALYSIS USING MACHINE LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 1, page no. ppa831-a834, January-2026, Available at : http://www.jetir.org/papers/JETIR2601098.pdf