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

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 <i>p</i>-test and Cohen's <i>d</i>-test to highlight the efficacy of the proposed method. To further validate the ranking of the features, we compared the <i>p</i>-values and Cohen's <i>d</i>-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.