A Critical Review of Machine Learning Models for Heart Disease Prediction
Volume: 13 - Issue: 12 - Date: 01-12-2024
Approved ISSN: 2278-1412
Published Id: IJAECESTU431 | Page No.: 105-109
Author: Bajrangi Kumar Gupta
Co- Author: Jeetendra Singh Yadav,,,
Abstract:-Heart disease remains a leading global health concern, necessitating accurate and early
prediction for improved patient outcomes. Machine learning (ML) has emerged as a powerful tool in
cardiovascular diagnostics, offering enhanced predictive capabilities over traditional methods. This review
critically evaluates various ML models, including logistic regression, decision trees, random forests, support
vector machines (SVM), artificial neural networks (ANNs), and deep learning techniques, in terms of
accuracy, interpretability, and real-world applicability.
The study highlights that ensemble learning and deep neural networks achieve high predictive performance
but face challenges such as data imbalance, interpretability, and computational demands. Recent
advancements in explainable AI (XAI), federated learning, and hybrid ML models aim to enhance model
reliability and clinical integration. The findings emphasize the need for a standardized evaluation framework
to improve ML adoption in healthcare.
This review provides key insights for researchers and clinicians, underscoring the potential of AI-driven
predictive analytics in revolutionizing heart disease diagnosis and personalized treatment
Key Words:-Heart Disease Prediction, Machine Learning, Deep Learning, Cardiovascular Diagnostics, AI in Healthcare, Predictive Analytics
Area:-Engineering
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