A review of machine learning methods to build predictive models for male reproductive health - Adimoelja, Ariawan; Firdaus Mahmudy, Wayan; Kurnianingtyas, Diva (IAES International Journal of Artificial Intelligence (IJ-AI), 2024)
Developing of artificial intelligence (AI) technology in the medical sector, especially in the part of male reproduction and infertility, is growing rapidly. In both supervised learning and unsupervised learning, AI has been tested and applied to medical personnel to treat their patients. Calculations from simple to complex probability and a combination of some different methods have conducted results of accurate and precise. The results can help determine the condition of male infertility. Artificial neural network (ANN) and fuzzy inference system (FIS) are AI techniques applied to male health issues. ANN is adequate for processing large amounts of combined data in a short time. ANN also has a high level of accuracy and excellent adaptive capabilities. Afterwards, FIS can reflect problems using models with easy to understand, flexible, and also competent to model complex linear functions for decision-making. Based on the advantages of ANN and FIS, it is hoped acquiring prediction results of better and more accurate in male health issues.