BO–FTT: A Deep Learning Model Based on Parameter Tuning for Early Disease Prediction from a Case of Anemia in CKD

Renal anemia (RA) is a common complication of chronic kidney disease (CKD). Patients with prolonged RA may present with nonspecific systemic manifestations, including cold intolerance, fatigue, drowsiness, anorexia, muscle weakness, reduced physical activity, impaired memory and cognitive function, and difficulty concentrating. Although previous studies have identified risk factors for anemia development in CKD, challenges remain in early diagnosis and therapeutic intervention. Therefore, we analyzed a dataset of CKD patients with RA from the MIMIC database and used machine learning models to predict whether RA will occur in CKD patients. In addition, an optimized model was designed, and is explained in the article, that tunes the hyperparameters of FT-Transformer (FTT) with the Bayesian optimization (BO) algorithm. The proposed BO–FTT model achieved an accuracy of 91.81%, outperforming the untuned FTT as well as TabNet, Multilayer Perceptron (MLP), and Kolmogorov–Arnold Networks (KAN) that were optimized by the BO algorithm.