Secondary Testosterone Deficiency Identification Using Hybrid Machine Learning Classifiers

Secondary testosterone deficiency (STD) occurs when the hypothalamus or pituitary gland malfunctions, preventing the testes from producing enough testosterone. STDs should be detected and treated early to improve quality of life and avoid long-term consequences. Even though heart disease and STDs have different underlying mechanisms, both often have a connection. Classifiers based on machine learning could improve the detection and treatment of both disorders. This represents a significant advancement in STD prediction using these classifiers. Studies comparing testosterone levels between people with and without heart disease were conducted to investigate potential connections. The development of a heart disease risk assessment model included low testosterone as a predictor. An extensive evaluation using assessment measures and multiple classification methods revealed how testosterone levels may influence heart disease risk. Both heart disease and STDs have distinct underlying mechanisms. The hypothalamus or pituitary gland, responsible for controlling testosterone synthesis, is implicated in STDs.