Evaluation of Machine Learning Techniques for Identifying Genetic Markers in Type 2 Diabetes: A Case Study of Federal University, Dutse, Jigawa State
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Background of the Study :
Type 2 Diabetes (T2D) is a multifactorial metabolic disorder influenced by both genetic and environmental factors, making the identification of genetic markers a complex yet essential task. With the advent of machine learning, new opportunities have emerged for analyzing large-scale genetic data to pinpoint markers associated with T2D. This study evaluates various machine learning techniques for identifying genetic markers that contribute to the risk and progression of Type 2 Diabetes. Recent developments in computational methods have shown promise in handling high-dimensional genomic datasets, allowing for the extraction of meaningful patterns that might be overlooked using traditional statistical approaches (Adebisi, 2023). By leveraging techniques such as random forests, support vector machines, and deep learning, the study aims to assess the performance of these methods in accurately identifying genetic markers linked to T2D (Ibrahim, 2024). The research is conducted in the context of Federal University, Dutse, Jigawa State, where local genetic data are employed to ensure the relevance of the findings. Comprehensive comparisons of model accuracy, sensitivity, and specificity are performed, highlighting the strengths and limitations of each approach. The study also explores ensemble methods that combine multiple algorithms to improve predictive performance. Additionally, the role of feature selection and data preprocessing is examined to enhance the models’ ability to detect subtle genetic signals. This work seeks to contribute to precision medicine by enabling earlier diagnosis and targeted interventions for T2D, thereby reducing the disease burden and associated healthcare costs (Balogun, 2025).
Statement of the Problem :
Identifying genetic markers for Type 2 Diabetes using machine learning techniques poses several challenges that hinder the realization of precision medicine. A major concern is the high dimensionality of genetic data, which can lead to issues such as overfitting and reduced generalizability of the models (Okoro, 2023). Many machine learning models struggle to differentiate between noise and true genetic signals, resulting in false positives or negatives. The heterogeneity of population data, combined with environmental influences, adds further complexity to identifying consistent genetic markers. Existing algorithms often require extensive tuning and large sample sizes to achieve reliable performance, resources that may be limited in local research settings. Moreover, the interpretability of machine learning models remains a significant challenge. The ‘black-box’ nature of some algorithms makes it difficult for clinicians to understand the genetic mechanisms underlying the predictions, thereby limiting clinical adoption (Nwankwo, 2024). Inadequate attention to data preprocessing, feature selection, and robust validation techniques further compromises the accuracy of the models. These issues highlight the need for a systematic evaluation of machine learning techniques within the specific context of Type 2 Diabetes research at Federal University, Dutse, Jigawa State. This study addresses these challenges by comparing different machine learning methods, optimizing model parameters, and employing robust validation techniques to ensure that the identified genetic markers are reliable and clinically relevant (Adetola, 2025).
Objectives of the Study:
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To evaluate various machine learning techniques for identifying genetic markers in Type 2 Diabetes.
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To optimize model parameters to enhance prediction accuracy and reliability.
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To validate the effectiveness of machine learning models using local genetic data.
Research Questions:
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Which machine learning technique offers the best performance in identifying genetic markers for T2D?
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How do feature selection and data preprocessing affect the accuracy of these models?
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What challenges are associated with applying machine learning to genetic data in T2D research?
Significance of the Study:
This study is significant as it provides critical insights into the application of machine learning for genetic research in Type 2 Diabetes, potentially enhancing early diagnosis and personalized treatment strategies. The findings could lead to more efficient use of local genetic data in precision medicine (Olu, 2024).
Scope and Limitations of the Study:
The study is limited to the evaluation and comparison of machine learning techniques for identifying genetic markers in Type 2 Diabetes at Federal University, Dutse, Jigawa State, and does not extend to clinical intervention or treatment trials.
Definitions of Terms:
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Machine Learning: A branch of artificial intelligence that uses statistical techniques to enable computers to learn from and make predictions based on data.
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Genetic Markers: Specific DNA sequences that are associated with particular traits or diseases.
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Type 2 Diabetes: A chronic condition affecting the way the body processes blood sugar, influenced by both genetic and environmental factors.
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