An Efficient Artificial Intelligence-Based Early Prediction of Heart Attack Using Deep Learning CNN and SVM Models

https://doi.org/10.5281/zenodo.17551611

Abstract

Artificial Intelligence (AI) has become an integral component of modern Cardiovascular disease diagnosis, treatment planning, and risk stratification. Its transformative potential is particularly crucial in developing regions. Cardiovascular issues cause most deaths worldwide. The World Health Organization states that about 17.9 million people die annually. Medical specialists spend considerable time diagnosing to identify the reason behind symptoms. Moreover, doctors cannot always keep up with their patients. Machine learning can assist doctors in predicting heart diseases by utilizing global health sector data, thus strengthening the base of this technology. The proposed system emphasizes temporal data modeling through the integration of Parallel CNN (P-CNN) and Support Vector Machine (SVM) classifiers to enable the early detection of Heart Failure (HF). This hybrid architecture facilitates the development of an effective decision-support system for the accurate diagnosis of Congestive Heart Failure (CHF). The model was trained and evaluated using the UCI Machine Learning Heart Disease dataset, and its performance was benchmarked against state-of-the-art algorithms such as Artificial Neural Networks (ANNs) and Recurrent Networks (RNs). Empirical evaluations reveal that CardioHelp achieves outstanding predictive performance, with an accuracy of 97%, an F1-score of 93.4%, and a precision improvement of 0.8% compared to existing baseline models. Furthermore, when compared with advanced hybrid architectures—including CNN-RNN, CNN-LSTM, and CNN-BLSTM—the proposed framework exhibits accuracy enhancements of 2.35%, 1.34%, and 1.95%,  Predicting heart disease accurately and quickly is important but difficult. Machine learning becomes highly effective in identifying and categorizing patients. Advanced machine learning and deep learning models, like SVMs and CNNs, can forecast patients' medical histories and general well-being. Heart disease predictions lower global death rates. While reducing the cost of medical care. Minimizing expensive hospital stays, long-term care, and urgent medical help can save lives and reduce family and societal stress. Using machine learning in healthcare can help find heart disease early, make sure patients get treatment on time, and help them live better lives. Moreover, using these technologies can help doctors make better decisions, increase the accuracy of diagnoses, and offer patients custom treatment plans