چکیده مقاله
Diabetes is a chronic metabolic disorder that has become one of the major global health challenges due to its high prevalence and severe complications Effective management of this disease requires proper blood sugar control and regular patient monitoring Recently, machine learning based techniques have been widely used for predictive modeling in healthcare, enabling more accurate forecasting and personalized interventions In this paper, we predict hospital readmission of diabetic patients using both traditional and advanced machine learning techniques The traditional models include XGBoost, LightGBM, CatBoost, Decision Tree, and Random Forest Moreover, we utilize an LSTM neural network, one of the most powerful modern machine learning models, to capture temporal dependencies To train and test the models, the Diabetes 130 US Hospitals dataset, containing 101,767 records with 50 features is used Results show that among traditional models, LightGBM performs the best, while the Transformer based model outperforms all traditional models and LSTM/CNN architectures by capturing long range temporal dependencies and heterogeneous clinical features more effectively In this work, we employ Explainable AI XAI techniques to enhance model interpretability and ensure decision making transparency Specifically, SHAP values are used to identify key factors influencing readmissions, such as the number of lab procedures and discharge disposition This study demonstrates that model selection, validation, and interpretability are key steps in predictive healthcare modeling This helps health providers design interventions for improved follow up adherence and better management of diabetes
کلیدواژهها
نویسندگان
Abolfazl Zarghani
Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Alireza Shorafa
Department of Computer Engineering, Shiraz University, Shiraz, Iran
شیوه ارجاع
Zarghani, Abolfazl and Shorafa, Alireza,1404,Time-Aware Transformer Framework for Diabetes Readmission Prediction,22th International Conference on Innovation and Research in Engineering Sciences (ICIRES)
ارائهشده در
مجموعه مقالات بیست و دومین کنفرانس بین المللی نوآوری و تحقیق در علوم مهندسی7 آذر 1404