Year: 2024
Type: article
Abstract: Heart disease remains a leading cause of mortality worldwide, necessitating early and accurate diagnosis to improve patient outcomes. Traditional diagnostic methods often rely on manual assessment and basic statistical models, which may not fully capture the complexity of cardiovascular conditions. This paper explores the application of advanced machine learning algorithms for heart disease prediction, leveraging large datasets and sophisticated feature extraction techniques. By comparing various machine learning models, including decision trees, support vector machines, and deep learning approaches, this research aims to identify the most effective predictive Model. The results demonstrate that these advanced techniques significantly enhance predictive accuracy, offering a promising tool for early detection and personalized treatment of heart disease
Authors: Prof. A. P. Tiwari, Prof. K. A. Kadu
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4402281937