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Optimization of the XG-Boost Algorithm for Predicting Stroke Patient Care Outcomes
Lestari, Puji
; Universitas Amikom Purwokerto
; Tahyudin, Imam
; (Scopus ID: 56764427700, Universitas Amikom Purwokerto)
; Tikaningsih, Ades
; Universitas Amikom Purwokerto
; Nurhopipah, Ade
; Universitas Amikom Purwokerto
Telematika
Vol 18
, No 1
(2025)
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 t...
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Optuna Based Hyperparameter Tuning for Improving the Performance Prediction Mortality and Hospital Length of Stay for Stroke Patients
Tikaningsih, Ades
; Universitas Amikom Purwokerto
; Lestari, Puji
; Universitas Amikom Purwokerto
; Nurhopipah, Ade
; Universitas Amikom Purwokerto
; Tahyudin, Imam
; (Scopus ID: 56764427700, Universitas Amikom Purwokerto)
; Winarto, Eko
; RSUD Banyumas Polyclinic Neural
; Hassa, Nazwan
; RSUD Banyumas Polyclinic Neural
Telematika
Vol 17
, No 1
(2024)
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 limit...
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