Understanding Diabetes among Malaysians using Logistic Regression: What Drives the Disease?
DOI:
https://doi.org/10.24191/jcrinn.v11i2.558Keywords:
Diabetes, Risk Factor, Logistic Regression Analysis, Malaysia, Risk PredictionAbstract
Diabetes is one of the critical illnesses that the rate of patients is increasing day by day not only in Malaysia, but across the globe. Yet, considering Malaysians' lack of acknowledgment of the early signs, they remain unaware of how serious and dangerous this disease is. Therefore, understanding the factors that cause diabetes is very important to ensure everyone involved can develop a preventive approach. This study aims to identify the factors that contribute to diabetes among Malaysians. A survey was conducted among Malaysians, and logistic regression analysis was utilized to determine the factors contributing to diabetes. The findings highlighted four leading risk factors that contribute to diabetes, which were age, body mass index, physical exercise, and smoking. From the analysis, the Omnibus test (p < 0.05) indicates that the independent variables collectively have a significant relationship with the presence of diabetes, while the Hosmer–Lemeshow test (p = 0.8799) confirms that the model fits the data well. The model explains 32.84% of the variation in diabetes based on Cox and Snell R², with a Nagelkerke R² of 0.7877, suggesting strong explanatory power. Additionally, the model demonstrates high predictive performance, with 98.86% sensitivity, 86.21% specificity, and an overall accuracy of 97.89%. These results offer a glimpse into how lifestyle choices interfere with the likelihood of someone developing diabetes. On top of that, these findings have aided in enhancing the prevention of diabetes and offer opportunities for further research to explore other diabetes-related factors.
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Copyright (c) 2026 Siti Fairusziah Khairul Anwar, Jaida Najihah Jamidin (Author)
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