DECISION SUPPORT FRAMEWORK FOR EARLY PREDICTION OF DIABETIC PATIENT READMISSION USING HYBRID METAHEURISTICS

Readmissions to hospitals are a significant health issue for people with diabetes and waste valuable hospital healthcare resources. The proposed model in this research, is a hybrid one combining feature selection using GRA and optimizing through PSO while tuning their hyperparameters by GWO. The most important predictors of risk of readmission from demographic, clinical and laboratory parameters were pre-processed and analyzed with the UCI Diabetic Readmission Database. GRA and PSO brought about good selection of informative features, and GWO tuned model parameters for base classifiers such as Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), Logistic Regression (LR) and k-Nearest Neighbours( KNN). The ensemble model reported here achieved the highest accuracy of 98.6+% compared with decision tree (DT) 97.3% and gradient boost (GB) of 97.6%. Other metrics – F1-score (98.5%), Kappa (0.987), AUC (0.999) further confirmed its robustness. Stratifying patients into high, moderate, low high risk enables earlier interventions and is a useful decision support system in diabetic readmission reduction initiatives and delivery of care.