Chaotic Grasshopper Optimization with Wavelet Kernel Extreme Learning Machine for Medical Data Classification

Medical data classification acts as a vital part to enhance the survival rate of the patients and control the health condition to a certain level depending upon the clinical and non-clinical profiles of the patients. To improve the survival rate and decision making process in healthcare sector, necessary disease diagnosis models become essential. This paper presents an efficient Chaotic Grasshopper Optimization Algorithm (CGOA) with Wavelet Kernel Extreme Learning Machine (WKELM), called CGOA-WELM for Medical Data Classification. The CGOA-WKELM model involves preprocessing at the beginning stage to raise the data quality. In addition, WKELM based classification process gets executed which involves the weighted approach to cost function for obtaining the identical effects of weighted ELM. At the same time, in order to effectively set the parameters of the WKELM model, CGOA is applied as a parameter optimizer in such a way to enhance the classification performance. An extensive experimental validation process gets executed to determine the betterment of the CGOA-WKELM model and the results are investigated interms of distinct aspects. The attained experimental values verified the superior performance of the CGOA-WKELM model on all the applied three datasets.