Year: 2025
Type: article
Abstract: : This study develops a machine learning model for predicting heart disease risk using patient data, including demographics, medical history, and clinical measurements. Various algorithms such as Decision Trees, Support Vector Machines (SVM), and Neural Networks are evaluated for their predictive accuracy. The aim is to assist clinicians in early diagnosis and intervention. The model is evaluated using accuracy, precision, recall, and F1-score, and focuses on building a robust tool for heart disease prevention
Authors: Rajendra Arakh, Priyanka Jain, Anshul Singh, Ansh Soni
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4410927405