Design and Implementation of an AI‐Based System for Predicting Genetic Disorders in Children: A Case Study of Federal University, Dutse, Jigawa State
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Background of the Study
Advances in artificial intelligence (AI) have significantly transformed medical diagnostics, enabling the rapid analysis of complex genetic data to predict disorders. At Federal University, Dutse, Jigawa State, the development of an AI‐based system for predicting genetic disorders in children aims to harness machine learning techniques to analyze high-dimensional genomic and clinical datasets. This platform integrates deep neural networks and ensemble learning models to detect patterns in DNA sequences that correlate with genetic anomalies, thereby facilitating early diagnosis and intervention (Adebayo, 2023). The system preprocesses raw sequencing data, performs variant calling, and applies predictive modeling to classify potential genetic disorders. By leveraging cloud computing, the platform ensures scalable processing, enabling real-time updates as new data becomes available (Ibrahim, 2024). Furthermore, the integration of data visualization modules allows clinicians to interpret complex genetic profiles intuitively. Interdisciplinary collaboration between computer scientists, geneticists, and pediatric specialists has driven the design of a robust system that not only improves prediction accuracy but also reduces the turnaround time for diagnostic results. The dynamic learning capability of the system, which continuously refines its predictive models based on incoming data, positions it as a critical tool in the fight against congenital disorders. By automating the identification process, the AI‐based platform minimizes human error and enhances the reliability of genetic screening programs. Ultimately, this research aims to provide a cost-effective, efficient, and accurate solution for early detection of genetic disorders in children, thereby enabling personalized treatment strategies and improved health outcomes (Chukwu, 2025).
Statement of the Problem
Despite advances in genomic sequencing, predicting genetic disorders in children remains challenging due to the complexity and heterogeneity of genetic data. At Federal University, Dutse, the absence of an integrated AI‐based diagnostic tool results in fragmented analyses and delayed clinical interventions (Bello, 2023). Traditional diagnostic methods, reliant on manual data interpretation, are labor-intensive and prone to errors, which delay timely treatment. Moreover, the rapid evolution of genetic markers necessitates a system capable of continuous learning and adaptation. Current methods lack the scalability required to handle large volumes of data, leading to inefficiencies in processing and interpretation. These challenges impede early diagnosis, contributing to suboptimal patient outcomes and increased healthcare costs. There is an urgent need for an automated, scalable platform that integrates high-throughput genomic data with advanced machine learning to predict genetic disorders accurately. Addressing these issues is critical for streamlining pediatric genetic screening and ensuring that vulnerable populations receive prompt, personalized care. This study aims to develop a comprehensive AI‐based system that not only improves diagnostic accuracy but also reduces the time from sample collection to diagnosis, thereby enhancing clinical decision-making and overall healthcare delivery (Okeke, 2024).
Objectives of the Study
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To design and develop an AI‐based system for predicting genetic disorders in children.
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To integrate high-throughput genomic data and machine learning algorithms for accurate prediction.
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To validate the system’s performance using real-world clinical datasets.
Research Questions
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How can machine learning algorithms be optimized to predict genetic disorders in children?
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What improvements in diagnostic accuracy and speed does the AI‐based system offer compared to conventional methods?
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How can the system be integrated into existing clinical workflows to enhance pediatric diagnostics?
Significance of the Study
This study is significant as it introduces an innovative AI‐based platform for early detection of genetic disorders in children, which is expected to enhance diagnostic accuracy and reduce healthcare costs. By leveraging advanced machine learning and scalable computing resources, the system will support personalized treatment and improve long-term health outcomes. The findings offer a transformative approach to pediatric diagnostics that can be adapted by other healthcare institutions, ultimately contributing to more effective and timely interventions (Adebayo, 2023).
Scope and Limitations of the Study
The study is limited to the design, implementation, and evaluation of the AI‐based system at Federal University, Dutse, focusing exclusively on genomic and clinical data analysis for predicting genetic disorders in children.
Definitions of Terms
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Genetic Disorder: A disease caused by abnormalities in an individual’s DNA.
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Machine Learning: Algorithms that enable computers to learn patterns from data for predictive analysis.
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Variant Calling: The process of identifying genetic variants from sequencing data.
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