Optuna Based Hyperparameter Tuning for Improving the Performance Prediction Mortality and Hospital Length of Stay for Stroke Patients

Abstract

Cardiovascular disease (CVD) stands as the foremost contributor to worldwide mortality, with strokes as part of significant CVD. Research on potential mortality risks and hospitalizations for stroke patients became crucial as a basis for evaluation to improve the quality and control of stroke patient services. Although machine learning technology has been widely used in health data analysis, understanding the relative performance and characteristics of machine learning (ML) models is still limited. Therefore, the study aims to broaden this understanding by comparing five ML models, namely XGBoost, Random Forest, Decision Trees, CatBoost, and Extra Trees, using stroke patient data from RSUD Banyumas Neural Poliklinik Indonesia. The model performance improvement process is the main focus, involving adjustments using the Optuna tuning library. Through this tuning approach, the key parameters of each ML model are optimally adjusted to improve their performance in predicting mortality risk and the duration of hospitalization for stroke patients. As a result, the XGBoost algorithm proved superior in predicting mortality (accuracy 86%, AUC 0.87) and the duration of hospitalization (accuracy 82%, AUC 0.79). This research has great potential to help hospitals identify high-risk stroke patients and plan more efficient treatment. This approach allows hospitals to use their resources better, improve medical services, and reduce unnecessary treatment costs.

Keywords

• stroke
• machine learning
• mortality
• length of stay
• prediction