Application of AI and XGBoost Algorithm in Sperm Analysis for Improved Diagnosis of Male Infertility

Abstract

This study explores the transformative potential of artificial intelligence, particularly the XGBoost algorithm, in enhancing sperm analysis for male infertility diagnosis. Its primary aim is to identify and characterize sperm abnormalities to enable timely interventions and personalized treatment, with a focus on improving diagnostic accuracy and effectiveness. The study evaluates how AI and XGBoost can analyze large datasets of sperm characteristics, uncovering subtle patterns indicative of infertility. The overarching objective is to develop an automated system capable of accurately classifying sperm samples based on criteria such as concentration, motility, and morphology. By leveraging machine learning and the computational power of AI, this research seeks to advance male infertility diagnosis, improve understanding of the complex factors contributing to infertility, and ultimately support more accurate diagnoses and better patient outcomes. Performance results from the MHSMA system show a Recall of 75.68, G-mean of 81.32, Precision of 82.54, and AUC of 82.21, demonstrating its promising diagnostic capability.