Male Fertility Classification using Machine Learning and Oversampling Techniques - Pradnya Sidhawara, Aloysius Gonzaga (Universitas Atma Jaya Yogyakarta, 2024)
Machine learning methods have been applied to male fertility diagnosis in recent years. Through early infertility case detection, this technology application offers potential benefits to the medical field. This study presents an experimental investigation that examines the prospect of using the oversampling technique and feature selection to enhance the performance of shallow classifiers to classify male fertility on the Fertility Dataset. Two oversampling techniques (SMOTE and ADASYN), two different scalers (MinMax and Standard), and two different feature selection methods (SelectKBest and SelectFromModel) were used to improve the performance of the classifier. The results show that the performance of machine learning models is better on the oversampled dataset than the original dataset. Random Forest performed best on the SMOTE test set with 90% accuracy, 89% and 100% Recall in Normal and Altered classes, respectively. Accidents or trauma, Age, and High Fevers features are selected by SelectKBest, and considered as factors that contribute to male fertility in prior studies.