A machine learning approach to predicting ovarian cancer: Integrating clinical and genetic data

Ovarian cancer (OC) is a deadly disease that affects a large number of women worldwide. Machine Learning (ML) models can help in the early detection of this disease, however, the use of these models may be limited by their lack of interpretability and the difficulty to evaluate their performance. In this work, five types of datasets were used, employing clinical features, different types of coding genomic features, and combining both. The use of interpretable ML (IML) models (one linear and one nonlinear model) provided us with better interpretability of the five feature sets. Following this study, nine binary classification models were compared, and the Accuracy, Recall, and Area Under the Curve were analyzed. The results showed that ML models employed the combination of clinical features and genomes with the coding of the position of genes in patients significantly improves the prediction. We demonstrated that the inclusion of different preprocessed patient data and especially through the information provided by IML models, can help clinicians to understand the disease better and make informed treatment decisions.