ISSN 1119-4618
 

Case Report 
JPAS. 2022; 22(2): 444-451


Modeling Effects of Diabetes Risk Factors Using Modified Logistics Regression

Alhaji Abdullahi Gwani, Alhaji Abdullahi Gwani, Alhaji Abdullahi Gwani, Ali B. Adamu, Abbas Umar Faruq.

Abstract
This paper model the effects diabetes risk factors using modified logistic regression model since OLSM is deficient especially when dealing with irreversible variables. A real data was sampled from MOPD unit, Federal Medical Centre Azare using systematic sampling technique. A total sampled of 407 patientsÂ’ data were used to test and compare the modified logistic regression model (MLRM) and the other competitive model. GRETL and SPSS programming bundles were utilized during the information analysis. The result reveals that MLRM is more efficient that LRM. Chi-square was employed to test for dependency between diabetes status (DBStatus) and the other suspected variables; it was shown that there is association between DBStatus and virtually all the variables incorporated in the MLRM model. The MLRM model can be used to predict the chance of an individual to be diabetic given data as input variables. Based on this model, it was observed that the reduced MLRM Model M2 is the suitable model than M1that is the Model with all the variables. This paper also revealed that blood pressure systolic, weight, height blood pressure diastolic, age and sugar level are statistically significant while the other remaining variables are not statistically significant. We recommend for further investigation using machine learning approach.

Key words: Model, Diabetes, Status, Patient , Data, Blood Pressure (BP), MLE, OLS and Probability


 
ARTICLE TOOLS
Abstract
PDF Fulltext
How to cite this articleHow to cite this article
Citation Tools
Related Records
 Articles by Alhaji Abdullahi Gwani
Articles by Alhaji Abdullahi Gwani
Articles by Alhaji Abdullahi Gwani
Articles by Ali B. Adamu
Articles by Abbas Umar Faruq
on Google
on Google Scholar

How to Cite this Article
Pubmed Style

Gwani AA, Gwani AA, Gwani AA, Adamu AB, Faruq AU, . Modeling Effects of Diabetes Risk Factors Using Modified Logistics Regression. JPAS. 2022; 22(2): 444-451.


Web Style

Gwani AA, Gwani AA, Gwani AA, Adamu AB, Faruq AU, . Modeling Effects of Diabetes Risk Factors Using Modified Logistics Regression. https://www.atbuscienceforum.com/?mno=45576 [Access: May 14, 2022].


AMA (American Medical Association) Style

Gwani AA, Gwani AA, Gwani AA, Adamu AB, Faruq AU, . Modeling Effects of Diabetes Risk Factors Using Modified Logistics Regression. JPAS. 2022; 22(2): 444-451.



Vancouver/ICMJE Style

Gwani AA, Gwani AA, Gwani AA, Adamu AB, Faruq AU, . Modeling Effects of Diabetes Risk Factors Using Modified Logistics Regression. JPAS. (2022), [cited May 14, 2022]; 22(2): 444-451.



Harvard Style

Gwani, A. A., Gwani, A. A., Gwani, A. A., Adamu, A. B., Faruq, A. U. & (2022) Modeling Effects of Diabetes Risk Factors Using Modified Logistics Regression. JPAS, 22 (2), 444-451.



Turabian Style

Gwani, Alhaji Abdullahi, Alhaji Abdullahi Gwani, Alhaji Abdullahi Gwani, Ali B. Adamu, Abbas Umar Faruq, and . 2022. Modeling Effects of Diabetes Risk Factors Using Modified Logistics Regression. Science Forum (Journal of Pure and Applied Sciences), 22 (2), 444-451.



Chicago Style

Gwani, Alhaji Abdullahi, Alhaji Abdullahi Gwani, Alhaji Abdullahi Gwani, Ali B. Adamu, Abbas Umar Faruq, and . "Modeling Effects of Diabetes Risk Factors Using Modified Logistics Regression." Science Forum (Journal of Pure and Applied Sciences) 22 (2022), 444-451.



MLA (The Modern Language Association) Style

Gwani, Alhaji Abdullahi, Alhaji Abdullahi Gwani, Alhaji Abdullahi Gwani, Ali B. Adamu, Abbas Umar Faruq, and . "Modeling Effects of Diabetes Risk Factors Using Modified Logistics Regression." Science Forum (Journal of Pure and Applied Sciences) 22.2 (2022), 444-451. Print.



APA (American Psychological Association) Style

Gwani, A. A., Gwani, A. A., Gwani, A. A., Adamu, A. B., Faruq, A. U. & (2022) Modeling Effects of Diabetes Risk Factors Using Modified Logistics Regression. Science Forum (Journal of Pure and Applied Sciences), 22 (2), 444-451.