AIoMT-Based Multi-Model Soft Computing Techniques for the Prediction of Heart Disease - GopiKrishna Pasam; Subhash Gupta; Srinivas G; Hameed Shaik (Iraqi Journal of Science, 2025)
The high rate of occurrence of heart disease these days needs immediate advancements implementation of predictive methodologies for early diagnosis with high precision to improve the disease treatment and reduce the mortality rate. This paper proposes an efficient prediction and alarming system that integrates the multi-model deep learning techniques, such as Radial Basis Function Neural Network (RBFNN), Support Vector Machines (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Backpropagation Neural Network (BPNN with the Artificial Intelligence of Medical Things (AIoMT) to enhance the ability of prediction of heart disease in the real-time environment. The proposed model offers a comprehensive solution that produces data-driven insights for accurate prediction and timely heart disease alerts. This study reveals the potential of AIoMT in transforming the healthcare sector by providing a forward-looking view into the control and management of cardiac health conditions of patients on a large scale. The results presented in this paper is evident to the effective impact of AIoMT technology on healthcare towards cardiac health management and preventive strategies.