Early detection of liver disorders using hybrid soft computing techniques for optimal feature selection and classification

Human liver disease is a sort of illness that starts in the ovaries and is particularly dangerous for women. As a consequence, aberrant cells develop that have the potential to spread to other parts of the body. Liver disease is a serious disorder that affects women's ovaries and is difficult to identify early on, which is why it is still one of the leading causes of mortality. The significance of unequivocal confirmation of intrinsic and typical components in establishing new frameworks to detect and eliminate danger is substantial. In this study, we present a hybrid soft computing approach for early diagnosis of liver problems (EDLD-HS). For high and low level feature extraction models, we first employ an improved ant swarm optimization (IASO) approach. Then, for optimum feature selection, we develop a modified whale search optimization (MWSO) technique that combines features that may reflect both texture patterns and semantic backdrop scattered in the data. After that, we employed a hybrid swallow swarm intelligent-deep neural network (HSSI-DNN) classifier to determine the stage of liver illness. Finally, we used MATLAB R2014a to test our proposed EDLD-HS method using well-known benchmark datasets including BUPA, ILPD, and MPRLPD. The simulation findings are compared to existing state-of-the-art methodologies in terms of accuracy, specificity, sensitivity, precision, recall, F1-score, G-mean, and area under curve (AUC). The detection accuracy of the proposed HSSI-DNN classifier is 83.26% (BUPA), 84.26% (ILPD), and 91.23% (ILPD) (MPRLPD).