On Univariate Gaussian Mixture Model for Diabetic Patients in Yola, Adamawa State
DOI:
https://doi.org/10.62054/ijdm/0102.17Keywords:
algorithm, Diabetes, mixture-model, EM, mclust, mixtoolsAbstract
Finite Mixture Model serves as a potent statistical tool for modeling intricate data distributions through the amalgamation of multiple Gaussian components. The aim of this work is to use finite mixture of normal distribution to model data on diabetic patients from Modibbo Adama University Teaching Hospital, Yola, Adamawa State, so as to expose latent sub-groups. The model is first used on synthetic data and subsequently on diabetic patients data. The diabetic data set consists of 553 cases of diabetes mellitus. The variables measured: Age(years), Mass of a patient(kg/meters), glucose level (plasma glucose con- centration, a 2-hour in an oral glucose tolerance test), pressure (Diastolic blood pressure mmHg), insulin (2-hour serum insulin mu U/ml) and class variable (0 or 1) treating 0 as false or negative and 1 treated as true or positive test for diabetes. The synthetic data is obtained from Gaussian distribution. The results showed that there is presence of heterogeneity in the data by showing the presence of 2 sub-groups.
References
Ahmed, H., Mohammed, M. B., and Baba, I. A. (2021). On Comparing Multi-Layer Perceptron and Logistic Regression For Classification Of Diabetic Patients In Federal Medical Center Yola, Adamawa State. International Journal of Engineering Technonologies and Management research, 8(6)
Aiello, E. M., Toffanin, C., Magni, L., and De Nicolao, G. (2022). Model- based identification of eating behavioral patterns in populations with type 1 diabetes. Control Engineering Practice, 123, 105128.
Alharithi, F., Almulihi, A., Bourouis, S., Alroobaea, R., and Bouguila, N. (2021). Discriminative learning approach based on flexible mixture model for medical data categorization and recognition. Sensors, 21(7), 2450.
Benaglia, T., Chauveau, D., Hunter, D. R., and Young, D. (2009). mixtools: An R Package for Analyzing Finite Mixture Models. Journal of Statistical Software, 32(6), 1–29. Retrieved from https://www.jstatsoft.org/v32/i06/
Cheng, K. K., Lam, T. H., and Leung, C. C. (2022). Wearing face masks in the community during the COVID-19 pandemic: altruism and solidarity. The Lancet, 399(10336), e39–e40.
Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the royal statistical society: series B (methodological), 39(1), 1–22.
Ganesalingam, S., and McLachlan, G. J. (1979). Small sample results for a linear discriminant function estimated from a mixture of nor- mal populations. Journal of Statistical Computation and Simu- lation, 9(2), 151–158.
Kayal, S., Bhakta, R., and Balakrishnan, N. (2023). Some results on stochastic comparisons of two finite mixture models with general components. Stochastic Models, 39(2), 363–382.
McLachlan, G., Lee, S. X., and Rathnayake, S. I. (2019). Finite mixture models. Annual review of statistics and its application, 6, 355– 378.
Rao, C. R. (1948). The utilization of multiple measurements in prob- lems of biological classification. Journal of the Royal Statistical Society. Series B (Methodological), 10(2), 159–203.
Scrucca, L., Fraley, C., Murphy, T. B., and Raftery, A. E. (2023). Model-Based Clustering, Classification, and Density Estimation Using mclust in R. Chapman and Hall/CRC. Retrieved from https://mclust-org.github.io/book/ doi: 10.1201/9781003277965
Sugasawa, S., Kim, J. K., and Morikawa, K. (2022). Semiparametric imputation using latent sparse conditional Gaussian mixtures for multivariate mixed outcomes. preprint arXiv:2208.07535 .
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Development Mathematics (IJDM)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors are solely responsible for obtaining permission to reproduce any copyrighted material contained in the manuscript as submitted. Any instance of possible prior publication in any form must be disclosed at the time the manuscript is submitted and a
copy or link to the publication must be provided.
The Journal articles are open access and are distributed under the terms of the Creative
Commons Attribution-NonCommercial-NoDerivs 4.0 IGO License, which permits use,
distribution, and reproduction in any medium, provided the original work is properly cited.
No modifications or commercial use of the articles are permitted.
