An Investigation of AI-Based Predictive Models for Genetic Risk Assessment: A Case Study of Federal University, Wukari, Taraba State
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Background of the Study
Advancements in artificial intelligence (AI) have revolutionized the field of genetic risk assessment by enabling the integration and analysis of complex genomic and clinical data. At Federal University, Wukari, Taraba State, researchers are investigating AI-based predictive models to assess the genetic risk of various diseases. The study employs deep learning, support vector machines, and ensemble methods to analyze high-dimensional data from genome-wide association studies (GWAS) and next-generation sequencing. These models can identify subtle patterns and interactions among genetic variants that contribute to disease susceptibility (Ibrahim, 2023). By automating the risk prediction process, AI can significantly reduce diagnostic turnaround time and improve the accuracy of predictions. The system is designed to learn continuously from new data, ensuring that its predictive performance improves over time. Cloud integration ensures scalability and real-time data processing, making it feasible to handle large datasets. This interdisciplinary research, involving bioinformaticians, data scientists, and clinicians, aims to create a robust platform that not only predicts genetic risk but also offers insights into underlying biological mechanisms. The approach also emphasizes interpretability, providing clinicians with actionable insights that can guide preventive measures and personalized treatment strategies. Overall, the study seeks to transform genetic risk assessment through the application of advanced AI models, thereby contributing to improved public health outcomes and paving the way for precision medicine (Chukwu, 2024).
Statement of the Problem
Despite significant progress in genetic research, accurately assessing genetic risk remains challenging due to the complexity of genetic interactions and the heterogeneity of clinical data. At Federal University, Wukari, the current risk assessment methods often rely on traditional statistical models that are inadequate for processing high-dimensional genomic data (Bello, 2023). These conventional approaches are limited in their ability to capture non-linear relationships among genetic variants, leading to suboptimal predictive accuracy. Furthermore, manual data integration and interpretation contribute to prolonged diagnostic processes, which can delay the implementation of preventive strategies. The lack of an integrated AI-based platform for genetic risk assessment hampers the ability of clinicians to identify individuals at high risk and to tailor interventions accordingly. This study proposes the development of an automated, scalable system that leverages AI to analyze multi-omics data for more accurate risk predictions. By addressing these challenges, the proposed system will enhance the speed and reliability of genetic risk assessment, ultimately supporting early intervention and personalized medicine initiatives. The integration of cloud computing will further ensure that the system can process large volumes of data efficiently. Overcoming these limitations is essential for reducing healthcare costs and improving patient outcomes through timely and precise genetic risk predictions (Okeke, 2024).
Objectives of the Study
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To develop an AI-based predictive model for genetic risk assessment.
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To integrate multi-omics data into the predictive framework.
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To evaluate the model’s accuracy and scalability in a clinical setting.
Research Questions
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How can AI algorithms enhance genetic risk prediction?
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What are the benefits of integrating multi-omics data in risk assessment models?
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How does the performance of the AI-based model compare with traditional methods?
Significance of the Study
This study is significant as it applies advanced AI methods to improve genetic risk assessment, enabling early detection and personalized preventive strategies. The developed platform will facilitate more accurate predictions, reduce diagnostic delays, and ultimately contribute to improved healthcare outcomes through precision medicine (Ibrahim, 2023).
Scope and Limitations of the Study
The study is limited to the design and evaluation of AI-based predictive models for genetic risk assessment at Federal University, Wukari, focusing solely on genomic and clinical data without extending to environmental factors.
Definitions of Terms
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Genetic Risk Assessment: The evaluation of an individual’s likelihood of developing a genetic disorder based on genomic data.
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Deep Learning: A subset of machine learning involving neural networks with multiple layers for pattern recognition.
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Ensemble Methods: Techniques that combine multiple models to improve predictive performance.
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