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All Journal Journal of Multidisciplinary Science: MIKAILALSYS Asian Journal of Science, Technology, Engineering, and Art Mikailalsys Journal of Advanced Engineering International Mikailalsys Journal of Mathematics and Statistics
Ogunmola, Adeniyi Oyewole
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Agriculture, Biological Sciences & Forestry Arts Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Electrical & Electronics Engineering Engineering Environmental Science Mathematics Mechanical Engineering Physics Social Sciences Other

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Journal : journal of multidisciplinary science mikailalsys

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Application of Quantile Regression and Ordinary Least Squares Regression in Modeling Body Mass Index in Federal Medical Centre Jalingo, Nigeria Ogunmola, Adeniyi Oyewole; Okoye, Benjamin Ekene
Journal of Multidisciplinary Science: MIKAILALSYS Vol 3 No 2 (2025): Journal of Multidisciplinary Science: MIKAILALSYS
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mikailalsys.v3i2.5322

Abstract

Body mass index is a measure of nutritional status of an individual. Malnutrition is a leading public health problem in developing countries like Nigeria, it is also a major cause of morbidity and mortality. In this study, Body mass index is modeled using ordinary least squares method and quantile regression method. Data is collected from Antiretroviral therapy Clinic in Federal Medical Centre, Jalingo. Variables in the data collected are the Body mass index, age, weight, height, sex and occupation of the patients. Results showed that the ordinary least square regression and quantile regression at 25th percentile, median percentile, 75th percentile and 95th percentile fit the data. Weight, age, sex and height of patients are significant in determining the BMI of the patients when OLS method is applied. While weight, sex and height of patients are significant in determining the BMI of the patients. It is also discovered that OLS method fits the data more than quantile regression method using AIC and MSE.
Application of Linear Probability Model to Road Traffic Crash Ogunmola, Adeniyi Oyewole; Ogebe, Victor Ajibo; Onowuzou, James Oruarooghene
Journal of Multidisciplinary Science: MIKAILALSYS Vol 3 No 2 (2025): Journal of Multidisciplinary Science: MIKAILALSYS
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mikailalsys.v3i2.5777

Abstract

Road traffic crashes remain a critical public health and safety concern, particularly in developing countries such as Nigeria, where they constitute one of the leading causes of mortality and injury. This study investigates the likelihood that a road traffic crash in each of Nigeria’s six geopolitical zones and in the country as a whole results in a minor incident. Quarterly data on road traffic crashes were sourced from the official database of the Federal Road Safety Corps and analyzed using a linear probability model. The model estimates the probability of a crash being categorized as minor across regions. Findings indicate that the probability of minor road traffic crashes is consistently below 20 percent in all zones and nationally, suggesting that the majority of reported crashes result in major damage or casualties. These results point to a concerning trend in crash severity across Nigeria. The study highlights the urgent need for enhanced traffic safety interventions, stricter enforcement of road regulations, improved vehicle and infrastructure standards, and more effective emergency response systems. Emphasizing preventative strategies and public awareness campaigns could help shift the balance toward more minor, less harmful outcomes when crashes do occur. Ultimately, the goal should be to ensure that in the event of a road traffic crash, the incident remains minor in nature, minimizing harm to life and property.
Estimation of Hypertension Prevalence Among Diabetic Patients with Respect to Certain Covariates Ogunmola, Adeniyi Oyewole; Uhembe, Solomon
Journal of Multidisciplinary Science: MIKAILALSYS Vol 3 No 2 (2025): Journal of Multidisciplinary Science: MIKAILALSYS
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mikailalsys.v3i2.6365

Abstract

This study investigates the risk factors associated with the prevalence of hypertension among diabetic patients in Jalingo, Taraba State, Nigeria, using logistic regression analysis. The results indicate a significantly high prevalence rate of hypertension among diabetic individuals, estimated at approximately 89.8%, with a 95% confidence interval ranging from 86.3% to 93.3%. The odds of a diabetic patient developing hypertension are about 8.8 times higher than not developing it. Logistic regression analysis identified systolic blood pressure and a family history of diabetes as significant predictors of hypertension. Specifically, a one-unit increase in systolic blood pressure corresponds to a 9.14% increase in the odds of being hypertensive, with the 95% confidence interval for the true odds ratio ranging from 5.59% to 12.82%. Additionally, diabetic patients with a family history of diabetes exhibit a 296.81% higher likelihood of developing hypertension compared to those without such a history, with the confidence interval for this odds ratio spanning from 7.65% to 1362.73%. These findings highlight the importance of monitoring systolic blood pressure and family history as key covariates in predicting hypertension risk among diabetic populations. Overall, the binary logistic regression model demonstrates robust predictive power for identifying hypertensive risk among diabetic patients based on these factors.
Comparing Univariate Time Series Forecast Methods for Malaria Fever Cases Ogunmola, Adeniyi Oyewole; Jibo, Yunusa Namale
Journal of Multidisciplinary Science: MIKAILALSYS Vol 3 No 2 (2025): Journal of Multidisciplinary Science: MIKAILALSYS
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mikailalsys.v3i2.6366

Abstract

This study evaluates the forecasting accuracy of three univariate time series models, Decomposition, Holt-Winter’s, and Seasonal Autoregressive Integrated Moving Average (SARIMA) for predicting monthly malaria fever cases from January 2008 to December 2024. Data were obtained from the Federal Medical Centre, Jalingo, and analyzed using the three models. Forecasting performance was assessed using Root Mean Square Error (RMSE) as the primary evaluation metric. Among the models, the SARIMA (0, 0, 1) × (1, 1, 2) demonstrated the lowest RMSE, indicating superior forecasting accuracy over the Decomposition and Holt-Winter’s methods. Seasonal trend analysis revealed that malaria fever cases tend to be higher from April to August, with June showing the highest seasonal index representing a 92% increase over the annual average. These findings highlight the SARIMA model’s effectiveness in capturing the seasonal patterns of malaria incidence and its utility for public health planning and intervention.
Co-Authors BAMIGBALA, Olateju Alao Idi, Danjuma Jibo, Yunusa Namale Jolayemi, Emmanuel Teju Ogebe, Victor Ajibo Okoye, Benjamin Ekene Onowuzou, James Oruarooghene Sambo, Garsama Uhembe, Solomon