Multi-phase feature selection for detection of epithelial ovarian cancer using ensemble machine learning techniques

Abstract

Epithelial ovarian carcinoma is one of the most prevalent causes of death. Timely ovarian cancer diagnosis is significant for bettering patient outcomes and rates of survival. For prognostic and diagnostic evaluation of malignancies, AI-based machine learning algorithms are used. This novel technique is undoubtedly an effective tool that may aid in selecting the best course of action. The collection of data comprising 150 patients contained an extensive selection of clinical characteristics and markers of tumors. The recursive feature elimination (RFE) and correlation coefficient feature selection techniques were assimilated to pick the features for the machine learning model, such as age, CA-125, tumor laterality, size, tumor type, grade of tumor, and International Federation of Gynecology and Obstetrics (FIGO) stage. The study’s findings indicate that the base model accuracy was around 96%, sensitivity 93%, and specificity 100%. Using ensemble classification, accuracy was around 96%, sensitivity 98%, and specificity 94% for the RFE technique. By obtaining a deeper understanding of their decision-making process, explainable artificial intelligence makes sophisticated machine learning methods easier to explain. Before beginning treatment, this research offers crucial data for the diagnosis and prognosis assessment of individuals with epithelial ovarian cancer (EOC).