PREDICTING THE RISK OF CARDIOVASCULAR DISEASES USING DEEP LEARNING MODELS

Cardiovascular disease (CVD) is one of the dominant causes of morbidity and mortality globally, promoting pressing needs for early, accurate, and efficient detection approaches. Traditional models (e.g., logistic regression, decision trees) yield crude absolute risk estimates that do not account for complex interactions of clinical, genetic, and lifestyle variables. In this study, we attempt to enhance the cardiovascular prediction using the Deep learning models, such as the Convolutional Neural Networks (CNN)6, Recurrent Neural Networks (RNN)7, and Deep Neural Networks (DNN)8. 70,000 records 2 12 dockized models of Deep Learning for predicting the probability of a heart attack. Dataset -Kaggle Cleaned and Balanced (age, blood pressure, cholesterol, and habits). The best test accuracy among them achieves up to 88.5% by a CNN model, and the precision-recall-F1-score is all more than 85%. As demonstrated, in terms of learning non-linear patterns and representing the high-dimensional information, the Deep learning framework significantly surpassed the traditional methods. While CNNs worked well on tabular features, RNNs added value when capturing time series for longitudinal prediction. Strengths and limitations. Though it had many strengths, the study did have some limitations, which included model interpretability, imbalanced data, and generalizability to demographics. The limitations of Deep learning from a computational standpoint and the lack of real-world validation were also discussed. In future work, we plan to further explore the hybrid CNN-LSTM models applied with the consideration of dynamic EHR, fairness for different age and ethnic groups, and also to introduce federated learning for privacy-preserving clinical deployment. This research demonstrated that Deep learning could have a dramatic transformation on precision medicine, enabling more accurate, scalable, and individualized risk assessment of CVD for early prediction, presymptomatic care, and clinical systems resources deployment.