Optimizing Machine Learning-Based Ovarian Cancer Prediction Through Normalization Strategies

Ovarian cancer is one of the most challenging cancers to detect early, often leading to poor survival rates. This study explores supervised and unsupervised machine learning and deep learning approaches to improve predictive performance using clinical and biomarker-based data which was scaled through two popular techniques: Min-Max scaling and Z-Score normalization. The research begins by carefully preprocessing the dataset including feature selection to ensure high-quality inputs. Various baseline and ensemble classifiers, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Logistic Regression (LR), are tested, for better model efficiency on both datasets. To further boost performance, ensemble methods like Stacking, Bagging, and Gradient Boosting, are incorporated. Additionally, unsupervised models like K-Means and DBSCAN clustering are implemented to study further subgroups of the Ovarian Cancer dataset optimizing results. The effects of different feature selection techniques and the impact of standardization versus normalization are compared on both datasets. The Min-Max normalization technique outperformed Z-Score and it is observed that, the Stacking classifier achieved the highest accuracy of 100%, followed by SVM, Logistic Regression, and Bagging, each recording an accuracy of 97%. Further, DBSCAN, a clustering technique outperformed K-Means with a Silhouette Score of 0.7245 and it is observed that clustering performed well with Min-Max when compared with Z-Score normalization technique. The findings highlight that a well-optimized combination of feature selection, ensemble learning, and clustering significantly enhances ovarian cancer prediction, providing a valuable foundation for early diagnosis and clinical decision support.