Document Type

Article

Publication Date

2024

DOI

10.4236/jilsa.2024.161002

Publication Title

Journal of Intelligent Learning Systems and Applications

Volume

16

Issue

1

Pages

12-22

Abstract

Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the Gradient Boosting Classifier and LSTM as top performers, demonstrating high accuracy and ROC AUC scores. Comparative analyses highlight model strengths and limitations, contributing valuable insights for optimizing predictive strategies. This study advances predictive analytics for cardiovascular health, with implications for personalized medicine. The findings underscore the versatility of intelligent systems in addressing health challenges, emphasizing the broader applications of ML and DL in disease identification beyond cardiovascular health.

Rights

© 2024 The Authors and Scientific Research Publishing Inc.

This work is licensed under the Creative Commons Attribution International License (CC BY 4.0).

Data Availability

Article states: "The dataset used in this study is available upon request, subject to privacy and ethical considerations."

Original Publication Citation

Pathan, S. M. K., & Imran, S. B. (2024). Integrated machine learning and deep learning models for cardiovascular disease risk prediction: A comprehensive comparative study. Journal of Intelligent Learning Systems and Applications, 16(1), 12-22. https://doi.org/10.4236/jilsa.2024.161002

ORCID

0000-0002-6271-2359 (Pathan)

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