Leveraging Random Forests for Ovarian Cancer Detection and Precision Prediction

This study has employed machine learning techniques for the critical task of ovarian cancer detection, utilizing a comprehensive dataset: Mendeley Data, V11. Our approach involved the application of a Random Forest classifier to a dataset comprising 50 relevant features. To mitigate the risk of overfitting and enhance model robustness, we randomly divided these features into subsets of 10, iteratively training and testing the classifier. The final prediction was based on the majority vote from five classifiers. Our results demonstrated remarkable success in ovarian cancer detection. The Random Forest classifier exhibited an outstanding accuracy of 0.98 on the test dataset, indicating its proficiency in distinguishing between ovarian cancer cases and non-cancer cases. Precision-Recall analysis further underscored the model's effectiveness, with an impressive average precision (AP) of 0.98. This high AP value underscores the model's capability to provide highprecision predictions, which is particularly crucial in medical applications, where minimizing false alarms is of paramount importance. These findings underscore the potential of machine learning, specifically Random Forest, as a valuable tool in ovarian cancer detection. The combination of feature subset selection and ensemble learning techniques led to a robust and reliable predictive model. While these results are promising, ongoing validation and collaboration with domain experts are essential steps toward the translation of this research into clinical practice, where early and accurate cancer detection can have a profound impact on patient outcomes.