Development of a Predictive Model for Studying the Genetics of Obesity in Nigerian Populations: A Case Study of Bingham University, Karu, Nasarawa State
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Background of the Study :
The increasing burden of obesity in Nigerian populations has raised significant concerns among healthcare professionals and policymakers. Genetic factors have emerged as crucial contributors to obesity, prompting the development of predictive models that incorporate genetic data to better understand risk factors. This study leverages genomic markers and machine learning techniques to construct a predictive model for obesity risk assessment. With obesity affecting both urban and rural communities, early identification of at-risk individuals is essential for implementing preventative measures and personalized interventions (Obinna, 2023). Recent advances in computational methods have allowed for the integration of diverse datasets, including genetic, environmental, and lifestyle factors, to capture the multifactorial nature of obesity (Adegoke, 2024). By using high-dimensional data and sophisticated algorithms, the model aims to improve the accuracy of obesity risk prediction. The integration of local genetic data is particularly important, as most models have historically relied on datasets from Western populations, potentially overlooking genetic variants prevalent in Nigerian cohorts (Nwankwo, 2025). Furthermore, the model is designed to be adaptable and scalable, ensuring that it can be implemented in various healthcare settings across the country. The research also emphasizes the importance of feature selection and data preprocessing to enhance model performance. The ultimate goal is to provide healthcare practitioners with a tool that not only predicts obesity risk with high precision but also supports targeted interventions and public health policies. By addressing the genetic predisposition to obesity, this study contributes to a growing body of literature that seeks to understand and mitigate the impact of obesity through personalized medicine and preventive healthcare strategies.
Statement of the Problem :
Despite advances in predictive modeling, significant challenges persist in accurately assessing obesity risk based on genetic data in Nigeria. A major problem is the limited availability of comprehensive genetic datasets that reflect the diversity of Nigerian populations. Researchers often rely on data from Western cohorts, which may not adequately capture the genetic predispositions unique to Nigeria (Eze, 2023). Furthermore, the complex interplay between genetic, environmental, and lifestyle factors poses additional challenges in developing models with high predictive accuracy. Existing models frequently fail to incorporate local socio-cultural influences, leading to potential misclassification of risk. Another concern is the technical complexity involved in integrating high-dimensional genetic data into a predictive framework, which can result in overfitting or underfitting of the model. This issue is compounded by inadequate computational resources in many local research settings. Moreover, the interpretability of machine learning models remains a challenge; clinicians may find it difficult to understand the underlying genetic mechanisms driving the predictions, which hampers the translation of findings into clinical practice. The lack of standardized protocols for data collection and analysis further exacerbates these problems. This study seeks to address these challenges by developing a predictive model tailored to Nigerian populations, incorporating both genetic and environmental factors, and validating its performance using local data. By doing so, it aims to enhance the early detection of obesity risk and support the implementation of personalized prevention strategies, ultimately improving public health outcomes (Adamu, 2025).
Objectives of the Study:
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To develop a predictive model integrating genetic markers and environmental factors for obesity risk assessment.
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To evaluate the model’s accuracy and applicability in Nigerian populations.
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To enhance clinical decision-making by providing an easy-to-use tool for early obesity risk detection.
Research Questions:
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What genetic markers are significantly associated with obesity in Nigerian populations?
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How can environmental and lifestyle factors be integrated into the predictive model?
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How effective is the developed model in predicting obesity risk compared to existing models?
Significance of the Study:
This study is significant as it provides a localized predictive model for obesity, enhancing early risk detection and personalized healthcare interventions. The model could inform public health policies and facilitate targeted prevention strategies, ultimately reducing obesity-related health burdens (Balogun, 2024).
Scope and Limitations of the Study:
The study is limited to the development and evaluation of a predictive model for obesity genetics using data from Bingham University, Karu, Nasarawa State, and does not extend to intervention implementation.
Definitions of Terms:
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Predictive Model: A computational algorithm designed to forecast outcomes based on input variables.
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Genetic Markers: Specific DNA sequences associated with a particular trait or condition.
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Obesity: A medical condition characterized by excessive body fat that increases the risk of health problems.
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