Medical Data Classification Using Jaya Optimized ELM

Abstract

The implication of machine learning approaches in healthcare issues makes the diagnosis and prognosis of cancer like deadly diseases easier and affordable. The sole aim of machine learning researchers is to design a robust classification model that can classify the medical data with high accuracy and less computational complexity. Here, Jaya optimized extreme learning machine (Jaya-ELM) is proposed for classifying medical data. To estimate the efficacy of the proposed model, two benchmark medical datasets, Lung Cancer and Breast Cancer, are collected from the UCI repository. The classification accuracy percentage is taken as a performance evaluation measure in this model. The result of this model is compared with the existing Neural Network (NN), Jaya optimized NN (Jaya-NN) and ELM models. The experimental result shows that the Jaya-ELM is significantly better than other models.