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In Journal of biomedical informatics ; h5-index 55.0

Heart disease remains one of the significantcauses ofmortality and morbidity amongst the world's population. Predicting heart disease is considered as one of the vital issues in clinical data analysis. Since the number of data is rising gradually, it is muchcomplicatedforanalyzing and processing,and especially, it becomes difficult to maintain the e-healthcare data. Moreover, the prediction model under machine learning seems to be anessentialfacet in this research area. In this scenario, this paper aims to propose a new heart disease prediction model with the inclusion of specificprocesses like Feature Extraction, Record, Attribute minimization,and Classification. Initially, both statistical and higher-order statistical features are extracted under feature extraction. Subsequently, the record and attribute minimization carried out, where Component Analysis PCA plays its major role in solving the "curse of dimensionality."Finally, the prediction process takes place by the Neural Network (NN) model that intake the dimensionally reduced features. Moreover, the major intention of this paper deals with the accurate prediction. Hence, it is planned to influence the utility of meta-heuristic algorithms for the weight optimization of NN. This paper introduces a new hybrid algorithm termed Particle Swarm Optimization (PSO) merged LA update (PM-LU) algorithm that solves the above-mentioned optimization crisis, which hybrids the concept of Lion Algorithm (LA) and PSO algorithm. Finally, the efficiency of proposed work is compared over other conventional approaches and its superiority is proven with respect to certain performance measures. From the analysis, the presented PM-LU-NN scheme with regards to accuracy is 3.85%, 12.5%, 12.5%, 3.85%, and 7.41% better than LM-NN, WOA-NN, FF-NN, PSO-NN and LA-NN algorithms.

Cherian Renji P


Feature Extraction, Heart disease, Neural Network, PSO merged LA update