Affiliations: [a] Department of Artificial Intelligence & Data Science, Karpagam College of Engineering, Coimbatore, India
| [b] School of Computing Science and Engineering, VIT Bhopal University, Madhya Pradesh, India
| [c] Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam, Erode Dt, India
Correspondence:
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Corresponding author. G. Karthikeyan, Assistant Professor, Department of Artificial Intelligence & Data Science, Karpagam College of Engineering, Coimbatore-641032, India. E-mail: gkarthikeyancse@gmail.com.
Abstract: With the vast advancements in the medical domain, earlier prediction of disease plays a substantial role in enhancing healthcare quality and making better decisions during tough times. This research concentrates on modelling and automated disease prediction model to offer an earlier prediction model for heart disease and the risk factors. This work considers a standard UCI machine learning-based benchmark dataset for model validation and extracts the risk factors related to the disease. The outliers and imbalanced datasets are pre-processed using data normalization to enhance the classification performance. Here, feature selection is performed using non-linear Particle Swarm Optimization (NL - PSO). Finally, classification is done with the Improved Deep Evolutionary model with Feed Forward Neural Networks (IDEBDFN). The algorithm’s learning nature is used to evaluate the nature of the hidden layers to produce the optimal results. The outcomes demonstrate that the anticipated model provides superior prediction accuracy. The simulation is carried out in a MATLAB environment, and metrics like accuracy, F-measure, precision, recall, and so on are evaluated. The accuracy (without features) of the evolutionary model in the UCI ML dataset is 97.65%, accuracy (with features) is 98.56%, specificity is 95%, specificity is 2% higher than both the datasets, F1-score is 40%, execution time (min) is 0.04 min, and the AUROC is 96.85% which is substantially higher than other datasets. The proposed model works efficiently compared to various prevailing standards and individual approaches.
