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 powerful tools for modeling complex, non-linear relationships in high-dimensional biological data. In genetic research, ANNs offer the potential to predict disease susceptibility by integrating genomic, clinical, and environmental datasets. At Federal University, Lafia, Nasarawa State, researchers are evaluating the efficacy of ANNs to predict individual disease risk. This study employs deep learning architectures—such as multi-layer perceptrons and convolutional neural networks—to analyze genome-wide data from diverse populations, aiming to identify subtle genetic variants that contribute to disease susceptibility (Ibrahim, 2023). The system incorporates advanced feature selection and regularization techniques to prevent overfitting, thereby ensuring that the predictive models remain robust and generalizable (Chukwu, 2024). Cloud computing resources are leveraged to handle the processing demands of high-throughput sequencing data, allowing real-time model updates and scalability. The interdisciplinary collaboration among data scientists, geneticists, and clinicians ensures that the ANN models are both statistically sound and clinically applicable. By automating the prediction process, the system is expected to reduce diagnostic delays and improve the precision of early interventions. Ultimately, the project aims to demonstrate that ANNs can significantly enhance disease susceptibility predictions, thereby supporting personalized medicine and leading to better patient outcomes (Adebayo, 2023).
Statement of the Problem
Despite the promising potential of ANNs in predictive analytics, their application in genetic risk assessment is still limited by challenges related to data complexity and model overfitting. At Federal University, Lafia, conventional risk prediction methods often fall short in capturing non-linear relationships between genetic variants and disease phenotypes, resulting in suboptimal predictions (Bello, 2023). Traditional statistical models are unable to process the vast, high-dimensional genomic data effectively, leading to high false-positive and false-negative rates. Moreover, the lack of standardized protocols for training and validating ANN models contributes to inconsistent outcomes, further impeding clinical adoption. This gap in technology delays early diagnosis and personalized treatment interventions, ultimately affecting patient outcomes. There is a critical need for an optimized ANN framework that can integrate multi-dimensional data, reduce overfitting, and enhance predictive accuracy. This study aims to address these challenges by developing a robust, scalable ANN-based system tailored for disease susceptibility prediction. By incorporating advanced techniques such as dropout regularization, ensemble learning, and hyperparameter tuning, the proposed model seeks to improve performance and generalizability. Addressing these issues is essential for translating genomic data into actionable clinical insights and advancing precision medicine initiatives (Okeke, 2024).
Objectives of the Study
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To develop and optimize ANN models for predicting disease susceptibility.
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To integrate genomic and clinical data into the prediction framework.
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To evaluate the model’s predictive accuracy and clinical applicability.
Research Questions
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How can ANN architectures be optimized for genetic risk prediction?
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What impact does multi-dimensional data integration have on model performance?
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How does the ANN-based model compare with traditional methods in predicting disease susceptibility?
Significance of the Study
This study is significant as it enhances disease susceptibility prediction using optimized ANN models, enabling early diagnosis and personalized treatment. The integration of comprehensive genomic data with advanced deep learning techniques is expected to improve predictive accuracy and support precision medicine initiatives, ultimately leading to better patient outcomes (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 that mimics biological neural networks to identify patterns in data.
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Overfitting: A modeling error where a function is too closely fitted to a limited set of data points.
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Genome-Wide Association Study (GWAS): A study that examines genetic variants across the genome to identify associations with diseases.
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