Explainable AI-based feature importance analysis for ovarian cancer classification with ensemble methods

• Published in: Frontiers in public health Vol. 13; p. 1479095
• Main Authors: Kodipalli, Ashwini, Devi, V. Susheela, Guruvare, Shyamala, Ismail, Taha
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 26.03.2025
• Subjects: Algorithms; Artificial Intelligence; bagging; boosting; Diagnosis, Computer-Assisted - methods; ensemble models; Female; Humans; interpretable AI; Machine Learning; Ovarian Neoplasms - classification; Ovarian Neoplasms - diagnosis; p-value; Public Health; SHAP; boosting; ensemble models; p-value; Cohen’s; bagging; interpretable AI; machine learning
• ISSN: 2296-2565; 2296-2565
• DOI: 10.3389/fpubh.2025.1479095

Ovarian Cancer (OC) is one of the leading causes of cancer deaths among women. Despite recent advances in the medical field, such as surgery, chemotherapy, and radiotherapy interventions, there are only marginal improvements in the diagnosis of OC using clinical parameters, as the symptoms are very non-specific at the early stage. Owing to advances in computational algorithms, such as ensemble machine learning, it is now possible to identify complex patterns in clinical parameters. However, these complex patterns do not provide deeper insights into prediction and diagnosis. Explainable artificial intelligence (XAI) models, such as LIME and SHAP Kernels, can provide insights into the decision-making process of ensemble models, thus increasing their applicability. The main aim of this study is to design a computer-aided diagnostic system that accurately classifies and detects ovarian cancer. To achieve this objective, a three-stage ensemble model and a game-theoretic approach based on SHAP values were built to evaluate and visualize the results, thus analyzing the important features responsible for prediction. The results demonstrate the efficacy of the proposed model with an accuracy of 98.66%. The proposed model's consistency and advantages are compared with single classifiers. The SHAP values of the proposed model are validated using conventional statistical methods such as the -test and Cohen's -test to highlight the efficacy of the proposed method. To further validate the ranking of the features, we compared the -values and Cohen's -values of the top five and bottom five features. The study proposed and validated an AI-based method for the detection, diagnosis, and prognosis of OC using multi-modal real-life data, which mimics the move of a clinician approach with a demonstration of high performance. The proposed strategy can lead to reliable, accurate, and consistent AI solutions for the detection and management of OC with higher patient experience and outcomes at low cost, low morbidity, and low mortality. This can be beneficial for millions of women living in resource-constrained and challenging economies.