Evaluation of the Use of Artificial Neural Networks in Predicting Disease Susceptibility: A Case Study of Federal University, Lafia, Nasarawa State
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Background of the Study
Artificial Neural Networks (ANNs) have emerged as a potent subset of machine learning, capable of modeling complex, non-linear relationships in high-dimensional data. In the context of genetic research, ANNs offer significant advantages for predicting disease susceptibility by integrating vast amounts of genomic, clinical, and environmental data. At Federal University, Lafia, Nasarawa State, researchers are evaluating the efficacy of ANNs to predict individual risk for various diseases based on genetic profiles. The study employs deep learning architectures, including multi-layer perceptrons and convolutional neural networks, to extract intricate patterns from genome-wide data sets (Ibrahim, 2023). These models are trained on diverse datasets that include genome-wide association study (GWAS) results and clinical risk factors, allowing them to capture subtle genetic variations that may predispose individuals to disease. The system is further enhanced by integrating cross-validation techniques and ensemble methods to improve prediction accuracy and mitigate overfitting (Chukwu, 2024). Cloud computing resources are leveraged to process large datasets in real-time, ensuring scalability and robustness. The interdisciplinary collaboration among data scientists, geneticists, and clinicians ensures that the predictive models are both statistically valid and clinically meaningful. Ultimately, the project aims to establish ANNs as reliable tools for disease susceptibility prediction, facilitating early diagnosis and personalized treatment strategies that can lead to improved patient outcomes and reduced healthcare costs (Adebayo, 2023).
Statement of the Problem
Despite the potential of Artificial Neural Networks in transforming predictive analytics, their application in genetic risk assessment remains underutilized. At Federal University, Lafia, traditional risk prediction models often fail to capture the complex interactions between genetic variants and environmental factors, resulting in suboptimal predictive performance (Bello, 2023). Conventional statistical models are limited in handling the high-dimensional nature of genomic data, leading to high false-positive and false-negative rates. The absence of an integrated ANN-based framework hampers the ability to generate reliable, actionable predictions, thereby delaying early diagnosis and personalized intervention strategies. Moreover, the lack of standardization in training and validation protocols for ANNs in this context further contributes to inconsistent results. There is a critical need to evaluate and optimize ANN architectures specifically for disease susceptibility prediction. This study seeks to address these challenges by developing a robust ANN-based system that incorporates comprehensive genomic and clinical datasets. By applying advanced techniques such as dropout regularization, ensemble learning, and hyperparameter optimization, the research aims to enhance the predictive accuracy and generalizability of the models. Addressing these limitations is essential for translating genomic data into meaningful clinical insights and for advancing personalized medicine initiatives. Improved prediction models will enable more precise risk stratification, leading to earlier and more effective therapeutic interventions, ultimately reducing the burden of disease and improving patient outcomes (Okeke, 2024).
Objectives of the Study
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To develop and optimize ANN models for disease susceptibility prediction.
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To integrate multi-dimensional genomic and clinical data into the prediction framework.
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To evaluate the model’s performance and clinical applicability.
Research Questions
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How can ANN architectures be optimized for predicting disease susceptibility?
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What is the impact of integrating multi-dimensional data on prediction accuracy?
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How does the ANN-based model compare with traditional prediction methods?
Significance of the Study
This study is significant as it advances the application of Artificial Neural Networks in predicting disease susceptibility, supporting early diagnosis and personalized treatment. The optimized models are expected to enhance predictive accuracy, reduce healthcare costs, and contribute to precision medicine initiatives (Ibrahim, 2023).
Scope and Limitations of the Study
The study is limited to developing and evaluating ANN models for disease susceptibility using genomic and clinical data at Federal University, Lafia, without extending to external validation or clinical trials.
Definitions of Terms
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Artificial Neural Network (ANN): A computational model inspired by biological neural networks that is used to recognize patterns in data.
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Overfitting: A modeling error that occurs when a function is too closely fit to a limited set of data points.
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Genome-Wide Association Study (GWAS): An observational study of a genome-wide set of genetic variants in different individuals to see if any variant is associated with a trait.
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