Research Article
Heart Disease Prediction using Machine Learning
@INPROCEEDINGS{10.4108/eai.28-4-2025.2357971, author={Lokesh Khedekar and Shail Kamtikar and Sarthak Kamtikar and Krishnakant Kale and Pranav Kamble and Eshan Kannawar}, title={Heart Disease Prediction using Machine Learning}, proceedings={Proceedings of the 4th International Conference on Information Technology, Civil Innovation, Science, and Management, ICITSM 2025, 28-29 April 2025, Tiruchengode, Tamil Nadu, India, Part II}, publisher={EAI}, proceedings_a={ICITSM PART II}, year={2025}, month={10}, keywords={heart attack prediction machine learning cardiovascular risk assessment medical diagnosis early detection predictive analytics heart disease clinical decision support artificial intelligence healthcare technology}, doi={10.4108/eai.28-4-2025.2357971} }- Lokesh Khedekar
Shail Kamtikar
Sarthak Kamtikar
Krishnakant Kale
Pranav Kamble
Eshan Kannawar
Year: 2025
Heart Disease Prediction using Machine Learning
ICITSM PART II
EAI
DOI: 10.4108/eai.28-4-2025.2357971
Abstract
Cardiovascular disease, and in particular myocardial infarction, is still a leading cause of death worldwide. Early prediction and timely interventions are important in reducing mortality. This paper presents a model for a Heart Attack Prediction System using machine learning algorithm that computes the cardiovascular risk based on essential clinical factors. It takes in chest patient information like age, blood pressure, cholesterol and lifestyle habits like whether the individual is a smoker to predict the likelihood of a heart attack. Performance evaluation indicates a correctness of 88.89%, which demonstrates that it could assist medical personnel in early diagnosis and preventive medicine. The system presented here targets better clinical decisions and reduced rates of hospital tradition and offers favorable benefits to patients.
