Applying machine learning techniques to the identification of late-onset hypogonadism in elderly men

Abstract

In the diagnosis of late-onset hypogonadism (LOH), the Androgen Deficiency in the Aging Male (ADAM) questionnaire or Aging Males’ Symptoms (AMS) scale can be used to assess related symptoms. Subsequently, blood tests are used to measure serum testosterone levels. However, results obtained using ADAM and AMS have revealed no significant correlations between ADAM and AMS scores and LOH, and the rate of misclassification is high. Recently, many studies have reported significant associations between clinical conditions such as the metabolic syndrome, obesity, lower urinary tract symptoms, and LOH. In this study, we sampled 772 clinical cases of men who completed both a health checkup and two questionnaires (ADAM and AMS). The data were obtained from the largest medical center in Taiwan. Two well-known classification techniques, the decision tree (DT) and logistic regression, were used to construct LOH prediction models on the basis of the aforementioned features. The results indicate that although the sensitivity of ADAM is the highest (0.878), it has the lowest specificity (0.099), which implies that ADAM overestimates LOH occurrence. In addition, DT combined with the AdaBoost technique (AdaBoost DT) has the second highest sensitivity (0.861) and specificity (0.842), resulting in having the best accuracy (0.851) among all classifiers. AdaBoost DT can provide robust predictions that will aid clinical decisions and can help medical staff in accurately assessing the possibilities of LOH occurrence.