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

Abstract

<b>Background/Objectives:</b> Postoperative complications such as wound infections and sepsis are common in diabetic patients, often resulting in longer hospital stays and higher morbidity. This study hypothesizes that a Random Forest Classifier can accurately predict these complications, enabling early clinical interventions. The model utilizes ensemble learning to integrate diverse patient data and improve predictive accuracy beyond traditional risk assessments. <b>Methods:</b> A comprehensive retrospective analysis was performed using data extracted from the National Surgical Quality Improvement Program (NSQIP) database. The dataset encompassed a wide array of variables, including demographic factors, clinical markers, and detailed surgical data (specialty, type of anesthesia, duration of surgery). Each variable was meticulously encoded into numerical formats, with categorical variables transformed through one-hot encoding, and continuous variables were normalized. The dataset was partitioned into training (80%) and testing (20%) subsets, ensuring a balanced representation of the target outcomes. The Random Forest Classifier was selected due to its robustness in handling high-dimensional data and its ability to model complex interactions between variables. <b>Results:</b> The Random Forest model showed accuracy rates of 94.38% for wound infection and 94.94% for sepsis. Precision and recall metrics also exceeded 94%, highlighting the model's accuracy in identifying true positives and reducing false positives. ROC curve analysis yielded AUC values of 0.92 for wound infection and 0.95 for sepsis, indicating strong discriminative capability. Feature importance analysis further identified key predictors, including surgical duration, specific laboratory markers, and patient comorbidities. <b>Conclusions:</b> 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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