Analysis and Evaluation of the Effects of Traditional Chinese Medicine Intervention on Patients with Diabetic Nephropathy in an Artificial Intelligence Environment
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Abstract
Current evaluations of traditional Chinese medicine intervention for diabetic nephropathy often lack data-driven predictive tools, resulting in limited objectivity and inconsistent outcome assessment. To construct an objective efficacy prediction method, this study included clinical data from 2,156 patients and extracted key variables through data preprocessing and feature engineering. Logistic regression, random forest, support vector machine, and XGBoost were used for model training and comparison. The results show that the XGBoost model achieved the best predictive performance, with an AUC of 0.902. Interpretability analysis indicates that baseline urinary protein, estimated glomerular filtration rate, and traditional Chinese medicine blood stasis syndrome are important predictive factors. Subgroup analysis shows that early-stage patients without significant blood stasis syndrome obtained the best efficacy, with an effectiveness rate exceeding 80%. By integrating artificial intelligence with clinical data analysis, this study demonstrates the feasibility of quantifying traditional Chinese medicine efficacy and provides support for personalized treatment evaluation. The modeling strategy also offers references for biomedical signal analysis, intelligent diagnosis, and data-driven sensing applications.
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