Prediction of secondary testosterone deficiency using a hybrid robust auto encoder and light gradient boosting model

Abstract Testosterone Deficiency (TD), secondary TD in particular, is an endocrine disease which is very common in elderly men but often underdiagnosed during diagnosis. It consists of a range of physical, psychological, and metabolic disturbances that include loss of libido, fatigue, and depression, while the possibility of cardiovascular complications seems to increase among those with TD. Therefore, determination of secondary TD should be done genuinely and as early as possible so that the patient can be subjected to early remedial intervention and life improvement. However, conventional diagnostic methods mainly depend on symptomatic and isolated measurements of hormones, and these measures may, therefore, be insufficient for reliable detection. This study introduces an efficient and robust machine learning framework for the prediction of secondary testosterone deficiency using clinical and biochemical data. Preprocessing of data began with Min-Max normalization, then followed by random sampling to maintain scale consistency and class balance. A Relational Autoencoder (RAE) for the extraction of important features and to maintain the reduced dimensionality and subsequent optimized LightGBM classifier using histogram optimization (HO) technique for prediction with much better performance. The developed model represents a high-performance system with 96.61% accuracy, 95.86% precision, 96.19% recall, specificity of 96.92%, and an F1 score of 96.03%. Experimental evaluations exhibit that the proposed model significantly outperforms traditional existing models against the values of accuracy, precision, recall, specificity, and F1-score.