Development of a Machine Learning-Based System for Identifying Mutations in Sickle Cell Disease: A Case Study of Federal University, Lokoja, Kogi State
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Background of the Study
Sickle cell disease (SCD) is a hereditary blood disorder resulting from mutations in the hemoglobin gene, leading to significant morbidity and mortality. Early detection of pathogenic mutations is essential for effective disease management. At Federal University, Lokoja, Kogi State, researchers are developing a machine learning-based system to identify mutations associated with SCD from genomic data. This system utilizes deep learning models, such as convolutional neural networks, to analyze high-throughput sequencing data and accurately detect genetic variations responsible for the disorder (Ibrahim, 2023). The platform automates the process of variant calling and annotation, reducing the need for manual interpretation and minimizing human error. By integrating clinical data, the system further refines its predictive models, ensuring high sensitivity and specificity in mutation detection (Chukwu, 2024). Cloud computing resources enable scalable analysis, making it feasible to process large datasets in real time. The interdisciplinary collaboration between geneticists, bioinformaticians, and clinicians ensures that the developed system is robust and clinically relevant. Ultimately, the tool aims to facilitate early diagnosis and enable personalized treatment strategies for individuals with sickle cell disease, contributing to improved patient outcomes and reduced healthcare costs (Adebayo, 2023).
Statement of the Problem
Sickle cell disease remains a significant health challenge in Nigeria, largely due to delays in accurate diagnosis and the limitations of conventional mutation detection methods. At Federal University, Lokoja, existing diagnostic approaches are time-consuming, require extensive manual curation, and often fail to detect low-frequency mutations accurately (Bello, 2023). Traditional methods lack the integration of machine learning technologies that can automate the detection process and improve predictive accuracy. Consequently, the diagnosis of SCD is often delayed, leading to suboptimal patient management and higher morbidity rates. There is a pressing need for a robust, automated system that can rapidly and accurately identify pathogenic mutations in the hemoglobin gene. This study proposes a machine learning-based system that leverages high-throughput sequencing data to overcome these challenges. By employing advanced deep learning models and integrating clinical parameters, the system aims to reduce diagnostic turnaround time and enhance the accuracy of mutation detection. Addressing these challenges is critical for enabling early intervention and improving treatment outcomes for patients with sickle cell disease. The successful implementation of this system could serve as a model for the adoption of AI-driven diagnostics in other genetic disorders (Okafor, 2024).
Objectives of the Study
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To develop a machine learning-based system for detecting mutations associated with sickle cell disease.
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To integrate high-throughput genomic data with clinical information for accurate prediction.
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To evaluate the system’s performance in terms of sensitivity, specificity, and diagnostic speed.
Research Questions
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How can machine learning improve the detection of mutations in sickle cell disease?
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What are the key genetic variants associated with SCD in the studied population?
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How does the system compare with traditional methods in terms of accuracy and efficiency?
Significance of the Study
This study is significant as it introduces an automated, machine learning-based system for the rapid detection of sickle cell disease mutations, which can significantly improve early diagnosis and patient management. The approach has the potential to reduce healthcare costs and enhance personalized treatment strategies in regions with a high prevalence of SCD (Ibrahim, 2023).
Scope and Limitations of the Study
The study is limited to the development and evaluation of the AI-based system at Federal University, Lokoja, focusing on genomic data analysis for SCD without extending to clinical trials.
Definitions of Terms
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Sickle Cell Disease: A genetic disorder characterized by abnormal hemoglobin leading to distorted red blood cells.
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Deep Learning: A subset of machine learning that uses multi-layered neural networks to model complex patterns.
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Variant Calling: The process of identifying genetic variants from sequencing data.
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