Cardiovascular diseases remain among the most critical healthcare challenges due to their complex risk factors, heterogeneous clinical characteristics, and the necessity for early intervention. Traditional diagnostic approaches often depend on manual interpretation of clinical parameters, which may introduce limitations in scalability, consistency, and predictive accuracy. Recent advancements in machine learning have created opportunities for developing intelligent prediction frameworks capable of identifying hidden patterns within patient health data. This research presents an intelligent machine learning framework for heart disease risk prediction using a hybrid integration of Support Vector Machine (SVM) and Artificial Neural Network (ANN) models. The proposed framework combines the strong classification capability of SVM with the nonlinear feature-learning ability of neural networks to enhance predictive performance and decision-support reliability. Previous studies have demonstrated the effectiveness of machine learning-based approaches for cardiovascular prediction, including ensemble learning, deep learning combinations, and individual classification models (Ahmed et al., 2022; Bharti et al., 2021). The methodology incorporates data preprocessing, feature optimization, hybrid model construction, and performance evaluation mechanisms to establish a robust predictive architecture. The study identifies that hybrid intelligent systems can improve disease risk assessment by balancing model generalization, computational efficiency, and predictive adaptability. Furthermore, the research highlights the importance of self-adaptive computational frameworks capable of handling dynamic healthcare data environments, where evolutionary and reasoning-based approaches can support continuous model improvement (Ramamurthy et al., 2026). The proposed framework provides a foundation for developing scalable healthcare analytics systems that assist clinicians in early diagnosis and personalized cardiovascular risk management. 

Heart Disease Prediction, Machine Learning, Support Vector Machine, Artificial Neural Network

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Banerjee Majumder, A., Gupta, S., & Singh, D. (2021). An ensemble heart disease prediction model bagged with logistic regression, Naïve Bayes and K Nearest neighbour. Journal of Physics: Conference Series, 2286(1), 012017.

Bharti, R., Khamparia, A., Shabaz, M., Dhiman, G., Pande, S., & Singh, P. (2021). Prediction of heart disease using a combination of machine learning and deep learning. Computational Intelligence and Neuroscience, 2021, 1–11.

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