ANALISIS SURVIVAL PASIEN DIABETES MELITUS TIPE-II MENGGUNAKAN MODEL REGRESI HAZARD ADITIF LIN-YING

Survival analysis is a statistical method used to analyze data whose response variable is time an event occurs. Lin Ying Additive Hazard Regression is a method that can be used to determine the effect of the independent variable on the response variable in survival analysis. This method can be used to determine differences in risk and there is no proportionality assumption that must be met so that it can be used for certain data. The purpose of this research is to analyze the characteristics of patients, determine the probability of improving the patient's clinical condition, and determine the factors that influence the length of stay of diabetic patients at RSUD R.A. Kartini Jepara. The response variable used is the length of time the patient was hospitalized. The independent variables were age, gender, history of hypertension, history of heart disease, history of stroke, history of kidney disease, history of obesity, and history of other diseases. The analysis results from Lin Ying additive hazard regression obtained factors that influence the time to improve clinical conditions, namely history of heart disease, history of kidney disease, history of stroke, and history of other diseases.

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Publication Details

Journal
Jurnal Gaussian
Published
2025-04-15
DOI
https://doi.org/10.14710/j.gauss.14.1.74-84
Primary Topic
Artificial Intelligence in Healthcare
Type
article
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article

ANALISIS SURVIVAL PASIEN DIABETES MELITUS TIPE-II MENGGUNAKAN MODEL REGRESI HAZARD ADITIF LIN-YING

Triastuti Wuryandari, Sudarno Sudarno, Harum Tsania Salsabila
Jurnal Gaussian
Artificial Intelligence in Healthcare
article

ANALISIS SURVIVAL PASIEN DIABETES MELITUS TIPE-II MENGGUNAKAN MODEL REGRESI HAZARD ADITIF LIN-YING

Triastuti Wuryandari, Sudarno Sudarno, Harum Tsania Salsabila
article en

Abstract

Survival analysis is a statistical method used to analyze data whose response variable is time an event occurs. Lin Ying Additive Hazard Regression is a method that can be used to determine the effect of the independent variable on the response variable in survival analysis. This method can be used to determine differences in risk and there is no proportionality assumption that must be met so that it can be used for certain data. The purpose of this research is to analyze the characteristics of patients, determine the probability of improving the patient's clinical condition, and determine the factors that influence the length of stay of diabetic patients at RSUD R.A. Kartini Jepara. The response variable used is the length of time the patient was hospitalized. The independent variables were age, gender, history of hypertension, history of heart disease, history of stroke, history of kidney disease, history of obesity, and history of other diseases. The analysis results from Lin Ying additive hazard regression obtained factors that influence the time to improve clinical conditions, namely history of heart disease, history of kidney disease, history of stroke, and history of other diseases.

Jurnal GaussianVol. 14(1)
Openalex Percentile: Top 80%
Artificial Intelligence in Healthcare
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ANALISIS SURVIVAL PASIEN DIABETES MELITUS TIPE-II MENGGUNAKAN MODEL REGRESI HAZARD ADITIF LIN-YING — Triastuti Wuryandari, Sudarno Sudarno, et al. · Jurnal Gaussian (2025) | TGRS Research Map | TGRS