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All Journal International Journal of Interdisciplinary Research
Ayobami Ibukun Okegbade
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Religion Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Languange, Linguistic, Communication & Media

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Time Series Modeling of Livebirths and Stillbirths: A Case Study of Obafemi Awolowo University Teaching Hospital Complex, Ile-Ife Ayobami Ibukun Okegbade; Abiola Opeyemi Egbewumi
International Journal of Interdisciplinary Research Vol. 1 No. 2 (2025): July 2025
Publisher : Ponpes As-Salafiyyah Asy-Syafi'iyyah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71305/ijir.v1i2.324

Abstract

: Livebirths and stillbirths are key public health indicators, with significant social and economic consequences. This study applies time series modeling to the quarterly data of livebirths and stillbirths recorded at Obafemi Awolowo University Teaching Hospital Complex (OAUTHC), Ile-Ife, spanning from 2001 to 2020. Using Augmented Dickey-Fuller tests, the data were confirmed to be stationary. Appropriate ARMA models ARMA(2,3) for livebirths and ARMA(1,3) for stillbirths were fitted based on minimum values of AIC, BIC, and HQIC. Forecasts show that livebirths are expected to fluctuate before stabilizing, while stillbirths are projected to remain relatively constants at around 30 cases per quarter. though with wide confidence intervals early in the forecast. However, the relatively low R2 values, especially for stillbirths, suggest that other unmeasured factors such as healthcare access, socio-economic conditions, or maternal risk factors may be influential. These findings underscore the importance of continuous improvement in maternal healthcare, data-driven planning and timely intervention.
Predicting Diabetes Mellitus Using Logistic Regression On Clinical And Demographic Data Ayobami Ibukun Okegbade; Abiola Opeyemi Egbewumi
International Journal of Interdisciplinary Research Vol. 2 No. 2 (2026): Vol 2 no 2 July 2026
Publisher : Ponpes As-Salafiyyah Asy-Syafi'iyyah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71305/ijir.v2i2.603

Abstract

Diabetes mellitus is a long term metabolic condition characterized by elevated blood glucose levels due to impaired insulin production, insulin action or both. The global rise in diabetes prevalence presents a major public health concern. This study utilized a dataset of 768 Diabetes Cases (No Diabetes (Type 1) = 500 Cases while Yes (Type 2) Diabetes Cases = 268 Cases) Obtained from Kaggle.com to explore the clinical and demographic predictors of diabetes mellitus using logistic regression analysis. Results revealed that glucose concentration, body mass index (BMI), diabetes pedigree function and number of pregnancies were the most significant predictors of diabetes. Elevated glucose emerged as he strongest predictor while obesity and hereditary risk substantially increased the likelihood of diabetes. The model demonstrated a good fit and moderate explanatory power, correctly classifying 78.3% of cases, though it performed better at identifying non-diabetic than diabetic individuals. Receiver Operating Characteristic (ROC) analysis confirmed glucose as the most discriminative variable followed by BMI and age whereas insulin, skin thickness and blood pressure contributed minimally. These findings reinforce the multifactorial etiology of diabetes emphasizing the combined influence of clinical, genetic and demographic factors in disease prediction. Clinically, the results suggest that regular monitoring of glucose levels, BMI and family history could enhance early detection and preventive management of diabetes in at risk populations.
Co-Authors Abiola Opeyemi Egbewumi