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Nurhopipah, Ade
Universitas Amikom Purwokerto
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Optuna Based Hyperparameter Tuning for Improving the Performance Prediction Mortality and Hospital Length of Stay for Stroke Patients Tikaningsih, Ades; Lestari, Puji; Nurhopipah, Ade; Tahyudin, Imam; Winarto, Eko; Hassa, Nazwan
Telematika Vol 17, No 1: February (2024)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v17i1.2816

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.
Optimization of the XG-Boost Algorithm for Predicting Stroke Patient Care Outcomes Lestari, Puji; Tahyudin, Imam; Tikaningsih, Ades; Nurhopipah, Ade
Telematika Vol 18, No 1: February (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i1.2817

Abstract

Stroke is a critical health issue in Indonesia, contributing to high mortality rates. At Banyumas District Hospital, stroke is the fourth most common condition, presenting significant challenges in both clinical care and financial management. The purpose of this study is to enhance the quality of services and optimize treatment costs for stroke patients by developing a predictive model using the XGBoost algorithm. This study employs the XGBoost algorithm to develop predictive models, which are then implemented within a web-based machine learning application using the Paython Flask framework. The models predict patient mortality and hospitalization duration. The results indicate that the XGBoost algorithm predicts patient mortality with 86% accuracy and hospitalization duration with 82% accuracy. The developed application significantly enhances care quality and resource management at Banyumas District Hospital by providing accurate predictions to support healthcare decision-making. The use of this application can significantly improve the management of stroke patient care at Banyumas District Hospital, thus maximizing service quality and optimizing treatment costs. By integrating accurate predictive modeling into healthcare decision-making processes, the application facilitates more effective allocation of resources and timely medical interventions, ultimately contributing to better patient outcomes.
Co-Authors Hassa, Nazwan Lestari, Puji Tahyudin, Imam Tikaningsih, Ades Winarto, Eko