Predictive Analytics for Diabetic Patient Care:Leveraging AI to Forecast Readmission and Hospital Stays  

作　　者：Saleh Albahli 

机构地区：[1]Department of Information Technology,College of Computer,Qassim University,Buraydah,51452,Saudi Arabia

出　　处：《Computer Modeling in Engineering & Sciences》2025年第4期1095-1128,共34页工程与科学中的计算机建模(英文)

摘　　要：Predicting hospital readmission and length of stay(LOS)for diabetic patients is critical for improving healthcare quality,optimizing resource utilization,and reducing costs.This study leveragesmachine learning algorithms to predict 30-day readmission rates and LOS using a robust dataset comprising over 100,000 patient encounters from 130 hospitals collected over a decade.A comprehensive preprocessing pipeline,including feature selection,data transformation,and class balancing,was implemented to ensure data quality and enhance model performance.Exploratory analysis revealed key patterns,such as the influence of age and the number of diagnoses on readmission rates,guiding the development of predictive models.Rigorous validation strategies,including 5-fold cross-validation and hyperparameter tuning,were employed to ensure model reliability and generalizability.Among the models tested,the RandomForest algorithmdemonstrated superior performance,achieving 96% accuracy for predicting readmissions and 87% for LOS prediction.These results underscore the potential of predictive analytics in diabetic patient care,enabling proactive interventions,better resource allocation,and improved clinical outcomes.

关 键 词：Machine learning healthcare classification predictive model DIABETES 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]