Oppositional Butterfly Optimization Algorithm with Multilayer Perceptron for Medical Data Classification

Abstract

Medical data classification can be assumed to be a crucial process in the domain of medical informatics. Generally, medical data comprises a set of medical records and literature which are considered as the essential healthcare data sources. But the existence of medical data includes complicated medical vocabulary and medical metrics makes the classification process challenging. Though several models are available in the literature, there is still needed to improve the classification performance. In this view, this paper devises a novel oppositional based learning with butterfly optimization algorithm (OBLBOA)and multilayer perceptron (MLP) called OBLBOA-MLP for medical data classification. The presented OBLBOA-MLP model involves three stages of operation such as preprocessing, classification, and parameter tuning. Primarily, data preprocessing is carried out to remove the unwanted data and raise the data quality to a certain extension. Besides, MLP model is applied as a classifier to determine the existence of the diseases. In addition, OBLBOA is employed for the hyperparameter optimization of the MLP model. The application of OBL helps to increase the performance of the BOA. A detailed set of simulation analysis was performed to determine the appropriate detection results of the OBLBOA-MLP model. The obtained experimental values pointed out the improved classification performance by attaining a higher accuracy of 98.23% and 92.67% on the applied CKD and skin disease dataset respectively.