Article
OVARIAN CANCER PREDICTION
Ovarian cancer is one of the most lethal gynecological malignancies worldwide due to its silent progression and late-stage diagnosis. Early detection significantly improves survival rates; however, conventional diagnostic approaches often fail to identify the disease at an initial stage. This study presents an Artificial Intelligence (AI)-driven Clinical Decision Support System (CDSS) designed to predict ovarian cancer risk using clinical and biochemical data. The proposed system integrates machine learning and deep learning algorithms to analyze multiple patient parameters, including age, menopausal status, hematological indicators, metabolic biomarkers, and tumor markers such as CA125, HE4, and AFP. The dataset is preprocessed through normalization, missing value handling, and feature selection to improve model accuracy. Various predictive models such as Logistic Regression, Support Vector Machines (SVM), Random Forest, and Deep Neural Networks are implemented and compared to identify the most reliable predictive model. Additionally, Explainable Artificial Intelligence (XAI) techniques, particularly SHAP (SHapley Additive Explanations), are employed to enhance transparency and interpretability by identifying the contribution of each biomarker in predicting cancer risk. The system architecture incorporates a scalable web-based platform with a FastAPI backend, a React frontend, and integrated database support for clinical data storage and user management. This enables healthcare professionals to input patient parameters and receive real-time predictions along with interpretable insights. Experimental results demonstrate that the proposed model achieves improved predictive performance compared to traditional diagnostic approaches. The developed system has the potential to assist clinicians in early screening, risk assessment, and decision-making, thereby improving early diagnosis and patient outcomes in ovarian cancer management.
Full Text Attachment
