Logistic Regression Analysis of Functional Constipation Factors in the Elderly
,
Abstract
Background: Machine learning software programs are of great interest at the scientific and applied levels in medical sciences today. There are various applications for these software programs in the field of diagnosis and treatment of diseases. Elderly people can benefit significantly from these software programs due to their physical limitations. The aim of this study is to develop and evaluate a supervised machine learning model for predicting functional constipation (FC) in the elderly.
Methods: The specific software in excel was designed as logistic regression supervised machine learning (LR-SML 402). This software development was based on a secondary analysis of source data, exclusive articles, and doctoral dissertations of elderly individuals with FC who underwent colorectal evaluations using advanced laboratory equipment. The correlation between labeled data and the output data of colorectal parameters was measured using 480 datasets from published sources and research labs. Strong correlations were obtained between variables such as age, body mass index, and Wexner's questionnaire with indicators of FC.
Results: To validate the performance of LR-SML 402, the results were compared with those of a neural network in SPSS software. The model designed in Excel software demonstrated strong capability in terms of sensitivity, specificity, and area under the curve (AUC).
Conclusion: The findings show that the supervised machine learning approach using logistic regression may provide meaningful clinical predictions in determining laboratory indicators of FC in the elderly. This approach can reduce the time and cost of diagnosis.
2. Wassan JT, Zheng H, Wang H. Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review. Cells. 2021 Nov;10(11):2924.
3. Grunhut J, Wyatt AT, Marques O. Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes. Journal of Medical Education and Curricular Development. 2021 Sep;8:23821205211036836.
4. Chen B. From Logistic Regression to Deep Learning: Machine Learning Advances
for Credit Card Churn. IJERMCA. 2024;13(2). 5. Ishii H, Kimino K, Aljehani M, Ohe N, Inoue M. An early detection system for dementia using the M2 M/IoT platform. Procedia Computer Science. 2016 Jan 1; 96:1332-40.
6. Ju R, Hu C, Li Q. Early diagnosis of Alzheimer's disease based on resting-state brain networks and deep learning. IEEE/ACM transactions on computational biology and bioinformatics. 2017 Nov 23;16(1):244-57.
7. Rescio G, Leone A, Siciliano P. Supervised machine learning scheme for
electromyography-based pre-fall detection system. Expert Syst Appl. 2018;100:95-
105.
8. Kutrani H, Eltalhi S, Ashleik N, editors. Predicting factors influencing survival of
breast cancer patients using logistic regression of machine learning. The 7th
International Conference on Engineering & MIS 2021; 2021
9. Mohsen S, Sadeghijam M, Talebian S, Pourbakht A. Use of some relevant parameters for primary prediction of brain activity in idiopathic Tinnitus based on a machine learning application. Audiol Neurotol. June 16, 2023;6-11
10. Salamat S, Montazeri V, Talebian S. Predicting trunk muscle activity in chronic low back pain: Development of a supervised machine learning model. J Mod Reh.2025;10-11
| Issue | Articles in Press | |
| Section | Research Article(s) | |
| Keywords | ||
| Chronic constipation elderly supervised logistic machine learning | ||
| Rights and permissions | |
|
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
