HEART DISEASE IDENTIFICATION ON METHOD USING MACHINE LEARNING CLASSIFICATION IN E-HEALT CARE

P.Narendra; P. Adhi lakshmi

doi:10.62643/

Authors

P.Narendra Author
P. Adhi lakshmi Author

DOI:

https://doi.org/10.62643/

Keywords:

Heart disease prediction, Machine learning, Logistic Regression, KNN, Random Forest, Feature selection, E-healthcare, Binary classification, Clinical decision support system

Abstract

Heart disease remains one of the leading causes of mortality worldwide, making early detection and timely intervention critical. This project, titled "Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare", aims to develop an intelligent, cost-effective cardiology solution to assist in the clinical decision-making process. Leveraging the power of machine learning, the system is designed to predict whether a patient is at risk of heart disease based on various medical parameters. The model performs binary classification where the outcome is either positive (1) — indicating the presence of heart disease — or negative (0) — indicating its absence. The project ensures that healthcare providers can access a reliable tool for diagnosing heart conditions, ultimately improving patient outcomes and optimizing healthcare resources. By integrating this model into electronic healthcare systems, it supports scalable, data-driven, and clinically appropriate care for patients with suspected cardiovascular conditions.