📅 27 February 2025
DOI: 10.35671/telematika.v18i1.2817
Optimization of the XG-Boost Algorithm for Predicting Stroke Patient Care Outcomes
Telematika
Universitas Amikom Purwokerto
📄 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.
🔖 Keywords
#XGBoost; Machine Learning; Web-Based Application; Extreme Programming (XP); Python Flask
ℹ️ Informasi Publikasi
Tanggal Publikasi
27 February 2025
Volume / Nomor / Tahun
Volume 18, Nomor 1, Tahun 2025
📝 HOW TO CITE
Lestari, Puji; Universitas Amikom Purwokerto; Tahyudin, Imam; (Scopus ID: 56764427700, Universitas Amikom Purwokerto); Tikaningsih, Ades; Universitas Amikom Purwokerto; Nurhopipah, Ade; Universitas Amikom Purwokerto; , "Optimization of the XG-Boost Algorithm for Predicting Stroke Patient Care Outcomes," Telematika, vol. 18, no. 1, Feb. 2025.
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