- Wednesday, Oct 29th, 2025
International Journal of Advanced Research in Arts, Science, Engineering & Management (IJARASEM)
(A High Impact Factor, Monthly, Peer Reviewed Journal)
(A High Impact Factor, Monthly, Peer Reviewed Journal)
| TITLE | Heart Disease Prediction using Machine Learning |
|---|---|
| ABSTRACT | Heart disease is a leading cause of death globally, making early and accurate prediction essential for effective treatment and prevention. This paper presents a comprehensive study on the application of various machine learning algorithms to predict heart disease using clinical data. We explore multiple classification techniques including Support Vector Machines (SVM), Decision Trees, Logistic Regression, K-Nearest Neighbors (KNN), Naive Bayes, Random Forest, and Gradient Boosting. The study utilizes the UCI Heart Disease dataset to train and evaluate these models. Our proposed system integrates data preprocessing, feature engineering, and hyperparameter tuning to enhance prediction accuracy and robustness. Experimental results demonstrate that ensemble methods like Random Forest and Gradient Boosting outperform traditional classifiers in terms of accuracy, precision, and recall. The system design emphasizes user-friendliness and scalability, making it suitable for deployment in clinical settings to assist healthcare professionals in early diagnosis and treatment planning. This work aims to reduce diagnostic errors, lower healthcare costs, and improve patient outcomes through data-driven decision support. |
| AUTHOR | J. Noor Ahamed, Sreejith M Assistant professor, Department of Computer Applications, Nehru College of Management, Coimbatore, Tamil Nadu, India Student of II MCA, Department of Computer Applications, Nehru College of Management, Coimbatore, Tamil Nadu, India |
| PUBLICATION DATE | 2025-10-27 |
| VOLUME | 12 |
| ISSUE | 5 |
| 13_Heart Disease Prediction using Machine Learning.pdf | |
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