All Title Author
Keywords Abstract

Publish in OALib Journal
ISSN: 2333-9721
APC: Only $99


Relative Articles


Integrated Machine Learning and Deep Learning Models for Cardiovascular Disease Risk Prediction: A Comprehensive Comparative Study

DOI: 10.4236/jilsa.2024.161002, PP. 12-22

Keywords: Cardiovascular Disease, Machine Learning, Deep Learning, Predictive Modeling, Risk Assessment, Comparative Analysis, Gradient Boosting, LSTM

Full-Text   Cite this paper   Add to My Lib


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.


[1]  World Health Organization (2021) Cardiovascular Diseases (CVDs).
[2]  Dey, D., Slomka, P.J., Berman, D.S., et al. (2019) Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review. Journal of the American College of Cardiology, 73, 1317-1335.
[3]  Attia, Z.I., Kapa, S., Lopez-Jimenez, F., McKie, P.M., Ladewig, D.J., et al. (2019) Screening for Cardiac Contractile Dysfunction Using an Artificial Intelligence-Enabled Electrocardiogram. Nature Medicine, 25, 70-74.
[4]  Cardiovascular-Disease-Dataset.
[5]  Hastie, T., et al. (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Vol. 2, Springer, New York.
[6]  Hochreiter, S. and Schmidhuber, J. (1997) Long Short-Term Memory. Neural Computation, 9, 1735-1780.
[7]  Chen, J.B., Song, L., Wainwright, M. and Jordan, M. (2018) Learning to Explain: An Information-Theoretic Perspective on Model Interpretation. Proceedings of the 35th International Conference on Machine Learning, 80, 883-892.
[8]  Pathan, S.M.K., et al. (2020) Wireless Head Gesture Controlled Robotic Wheel Chair for Physically Disable Persons. Journal of Sensor Technology, 10, 47-59.
[9]  Pathan, S.M.K. and Ali, M.F. (2019) Implementation of Faster R-CNN in Paddy Plant Disease Recognition System. 2019 3rd International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE), Rajshahi, 26-28 December 2019, 189-192.
[10]  Pathan, S.M.K. and Rana, M.M. (2022) Investigation on Classification of Motor Imagery Signal Using Bidirectional LSTM with Effect of Dropout Layers. 2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE), Gazipur, 24-26 February 2022, 1-5.
[11]  Bashar, L.B., Arifin, S. and Pathan, S.M.K. (2020) Numerical Analysis of Scale Effect on Performance of DU84-132 Airfoil for Small Wind Turbine Blade. International Conference on Mechanical, Industrial and Energy Engineering, Khulna, 19-21 December 2020.


comments powered by Disqus

Contact Us


WhatsApp +8615387084133

WeChat 1538708413