Abstract

Wearable technology has become an element of health tracking and occupies a substantial place in growing the Internet of Medical Things (IoMT). IoMT can minimize the mortality rate due to the early diagnosis of the diseases. Although this area is improving, there is still a way to increase the accuracy of prediction. To resolve this, the paper proposes Soft computing enforced Adaptive Neuro-Fuzzy Inference System (ANFIS) model for heart disease prediction, designed to ensure reliable diagnosis. The model integrates fuzzy logic and neural networks, optimized by Particle Swarm Optimization (PSO), to address challenges such as noisy data and local minima in training. Additionally, privacy-preserving mechanisms are incorporated, ensuring optimal data flow and compliance with healthcare regulations: Feature Extraction using Particle Swarm Optimization (PSO) helps in finding the pertinent features of clinical data and also finds the best feature space of clinical data so that the most promising features can be selected to make reliable predictions. Adaptive Neuro-Fuzzy Inference System (ANFIS), which can be used to classify heart disease data (and apply neural network and fuzzy logic), suffers problems with local minima because of the gradient nature of this learning. The hybrid ANFIS-PSO model enhances diagnostic accuracy by improving feature extraction and parameter optimization, yielding higher performance than traditional models. The hybridisation of PSO and ANFIS allows attaining higher predictive accuracy and system reliability, highlighting the significance of the optimisation of the feature extraction and parameters setting to gain optimum performance of the health monitoring application.