To construct a diagnostic and prediction model based on machine learning for fatigue status in patients with diabetic kidney disease (DKD), so as to provide a basis for early screening and intervention.

A retrospective analysis was conducted on patients with DKD admitted to the Seventh Affiliated Hospital of Anhui University of Chinese Medicine from January 2023 to June 2025. The patients were divided into a fatigue group (score ≥ 4 points) and a non-fatigue group (score < 4 points) according to the Fatigue Severity Scale (FSS). Demographic, clinical, laboratory, and medication data of the patients were collected. Robust features were screened via LASSO regression. Repeated stratified nested cross-validation was adopted. The predictive performances of LASSO logistic regression, Random Forest (RF), and Light Gradient Boosting Machine (LightGBM) models were compared. And the clinical application value of the models was evaluated using decision curve analysis.

A total of 166 patients with DKD were enrolled, including 78 cases in the fatigue group and 88 cases in the non-fatigue group. The incidence of fatigue among the DKD patients was 46.99% (78/166). Eleven robust features were screened out via LASSO regression, namely estimated glomerular filtration rate, hemoglobin, albumin, urinary albumin/creatinine ratio, age, carbon dioxide combining power, glycated hemoglobin, diuretic use, sleep disorder, heart failure, and sodium-glucose cotransporter 2 inhibitor use. Internal validation results showed that the LightGBM model achieves the best predictive performance, with an area under the receiver operating characteristic curve of 0.836, a Brier score of 0.175, a sensitivity of 0.756, and a specificity of 0.795. The calibration curve indicated that the model had a good calibration effect. And the decision curve analysis revealed that this model yielded good clinical net benefit within the threshold range of 0.05-0.60.

The diagnostic prediction model built based on LightGBM effectively identified the fatigue status in the DKD patients, facilitating early clinical risk stratification and intervention decision-making.