DiabCompSepsAI: Integrated AI Model for Early Detection and Prediction of Postoperative Complications in Diabetic Patients—Using a Random Forest Classifier

Abstract

This study demonstrates the Random Forest Classifier's strong predictive ability for postoperative wound infections and sepsis in diabetic patients. The model's high-performance metrics indicate its potential for real-time risk stratification in clinical workflows. Future research should validate these findings in diverse populations and surgical settings. Incorporating this predictive model into clinical practice has the potential to significantly improve patient outcomes and reduce healthcare costs.

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