Intelligent Heart Disease Diagnosis: Analyzing Predictive Accuracy of Machine Learning Models
Volume: 14 - Issue: 02 - Date: 01-02-2025
Approved ISSN: 2278-1412
Published Id: IJAECESTU445 | Page No.: 108-115
Author: Manoli Balasaheb Charmal
Co- Author: Dr. Tripti Arjariya
Abstract:-Heart disease remains a leading cause of mortality worldwide, necessitating early
diagnosis and preventive healthcare strategies. With the increasing integration of machine learning
(ML) into medical analytics, predictive models have become instrumental in enhancing the accuracy
and efficiency of heart disease diagnosis. This review investigates recent advancements in heart disease
prediction using ML algorithms,Emphasis is placed on widely adopted models such as Decision Trees,
Random Forest, Naive Bayes, Logistic Regression, K-Nearest Neighbors, and XGBoost.These
algorithms have demonstrated significant potential in identifying complex patterns in clinical datasets,
surpassing traditional statistical methods in adaptability and predictive power. Various studies have
highlighted the strengths of each algorithm: Decision Trees and Logistic Regression offer high
interpretability, while ensemble techniques like Random Forest and XGBoost deliver superior accuracy
and robustness. Naive Bayes proves effective with limited data, and KNN is noted for its performance
in normalized, noise-free environments.The review also discusses the relevance of datasets like the UCI
Cleveland Heart Disease Dataset and the critical role of preprocessing techniques such as
normalization, imputation, and feature selection. Evaluation metrics including accuracy, precision,
recall, F1-score, and ROC-AUC are analyzed for their effectiveness in assessing diagnostic
performance. Furthermore, hybrid and ensemble methods have shown promise in boosting predictive
outcomes through model integration and optimization.This paper concludes by emphasizing the
importance of algorithm selection, data quality, and preprocessing in developing reliable ML-based
heart disease prediction systems. The insights presented aim to guide future research and support
clinical decision-making through intelligent, data-driven solutions.
Key Words:-Heart Disease, Coronary Artery Disease (CAD), Cardiovascular Risk, Global Health, WHO, Mortality, Public Health Burden, Modifiable Risk Factors, Preventive Healthcare, Atherosclerosis
Area:-Engineering
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