Fuzzy Logic with Archimedes Optimization Based Biomedical Data Classification Model

Abstract

Medical data classification becomes a hot research topic in the healthcare sector to aid physicians in the healthcare sector for decision making. Besides, the advances of machine learning (ML) techniques assist to perform the effective classification task. With this motivation, this paper presents a Fuzzy Clustering Approach Based on Breadth-first Search Algorithm (FCA-BFS) with optimal support vector machine (OSVM) model, named FCABFS-OSVM for medical data classification. The proposed FCABFS-OSVM technique intends to classify the healthcare data by the use of clustering and classification models. Besides, the proposed FCABFS-OSVM technique involves the design of FCABFS technique to cluster the medical data which helps to boost the classification performance. Moreover, the OSVM model investigates the clustered medical data to perform classification process. Furthermore, Archimedes optimization algorithm (AOA) is utilized to the SVM parameters and boost the medical data classification results. A wide range of simulations takes place to highlight the promising performance of the FCABFS-OSVM technique. Extensive comparison studies reported the enhanced outcomes of the FCABFS-OSVM technique over the recent state of art approaches.