a modified metaheuristic algorithm integrated elm model for cancer classification

Background: in the rapidly defiled environment, cancer has emerged out as the most threatening disease to human species. therefore, a robust classification model is required to diagnose cancer with high accuracy and less computational complexity.method: here, random parameters of extreme learning machine (elm) are optimized by self adaptive multi-population-based elite strategy jaya (sampej) algorithm. this strategy constructs a robust elm classifier named as sampej-elm model. this model is tested on breast cancer, cervical cancer and lung cancer datasets. here, a comparative analysis is presented between the proposed model and basic elm, jaya optimized elm (jaya-elm), teaching learning based optimization (tlbo) optimized elm (tlbo-elm), sampej optimized neural network (sampej-nn), sampej optimized functional link artificial neural network (sampej-flann) models. numerous performance metrices viz. accuracy, specificity, gmean, sensitivity, f-score with receiver operating characteristic (roc) curve are used to estimate the proposed model. moreover, this model is compared with eleven existing models.results: sampej-elm model resulted the highest degree of accuracy, sensitivity and specificity in breast cancer (.9895, 1, .9853), cervical cancer (.9822, .9948, .9828), lung cancer (.9787, 1, 1) datasets. conclusion: the experimental results reveal that sampej-elm model classifies both the positive and negative samples of cancer datasets significantly better than others.