A Comprehensive Review on AI-Driven Prediction Model for Early Detection of Ovarian Cancer

This study investigates the application of deep learning (DL) and machine learning (ML) for the detection of ovarian cancer, which is frequently referred to as the "silent killer" because of its early asymptomatic phases. Ovarian cancer can be successfully predicted using machine learning (ML) models such as Random Forests, Support Vector Machines, K-Nearest Neighbours, and advanced convolutional neural networks (CNNs). Interestingly, a Random Forest approach obtained 98% accuracy, while CNNs optimised with Krill Herd Optimisation have overcome the shortcomings of earlier models. In order to improve patient outcomes, enable early intervention, and increase diagnostic accuracy, the study highlights the use of explainable AI (e.g., SHAP), ensemble learning, and feature engineering.