Design of a Machine Learning Model for Predicting Breast Cancer Susceptibility: A Case Study of Federal University, Birnin Kebbi, Kebbi State
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Background of the Study
Breast cancer remains one of the most prevalent malignancies affecting women worldwide, with genetic factors playing a significant role in individual susceptibility. The advent of machine learning has opened new avenues for predicting breast cancer risk by analyzing complex genomic and clinical data. At Federal University, Birnin Kebbi, researchers are designing a machine learning model aimed at predicting breast cancer susceptibility with high accuracy. This approach leverages advanced algorithms such as support vector machines, random forests, and deep neural networks to identify subtle patterns in genetic data that may indicate an increased risk of developing breast cancer (Aminu, 2023). The integration of clinical parameters, including age, family history, and lifestyle factors, further enhances the predictive power of the model. By analyzing high-dimensional data from genome-wide association studies (GWAS) and gene expression profiles, the model seeks to uncover novel genetic markers and gene-environment interactions that contribute to breast cancer development (Ibrahim, 2024). The study emphasizes the importance of data preprocessing, feature selection, and model validation to ensure the reliability and robustness of the predictions. In addition, cross-validation techniques and ensemble learning methods are employed to minimize overfitting and improve generalizability. The interdisciplinary nature of this research, combining expertise from oncology, genetics, and data science, aims to bridge the gap between computational predictions and clinical decision-making. Ultimately, the machine learning model is envisioned as a tool for early detection and personalized risk assessment, enabling proactive intervention strategies and improved patient outcomes. This innovative approach not only has the potential to reduce breast cancer mortality rates but also to inform tailored treatment plans based on individual genetic profiles (Chukwu, 2025).
Statement of the Problem
Despite advancements in genetic research and machine learning, predicting breast cancer susceptibility remains challenging due to the complex interplay of genetic, environmental, and lifestyle factors. At Federal University, Birnin Kebbi, existing predictive models often suffer from limitations such as high false-positive rates, inadequate incorporation of heterogeneous data, and lack of interpretability. These issues stem from the difficulty in extracting meaningful patterns from high-dimensional genomic data and the variability in clinical factors among individuals (Bello, 2023). Current methods that rely solely on statistical correlations are unable to fully capture non-linear relationships inherent in genetic data, leading to suboptimal risk stratification. Moreover, many available models have been developed using datasets that do not represent the diverse populations in which breast cancer manifests, thereby reducing their applicability in the local context. This research seeks to address these gaps by developing a machine learning model specifically tailored to the genetic and clinical profiles of the population served by Federal University, Birnin Kebbi. The study will integrate data from GWAS, gene expression studies, and clinical records to build a comprehensive predictive model. Emphasis will be placed on improving the model’s accuracy and interpretability, ensuring that the predictions are actionable for clinical decision-making. Addressing these challenges is crucial for reducing breast cancer mortality through early detection and targeted prevention strategies (Okafor, 2024).
Objectives of the Study
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To design a machine learning model that integrates genomic and clinical data for predicting breast cancer susceptibility.
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To validate the predictive performance of the model using local patient datasets.
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To enhance model interpretability to support clinical decision-making.
Research Questions
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How can machine learning models be optimized to predict breast cancer susceptibility accurately?
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What genetic and clinical factors are most indicative of breast cancer risk in the local population?
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How can the model’s predictions be effectively translated into clinical practice?
Significance of the Study
This study is significant as it leverages machine learning to improve early detection of breast cancer, potentially reducing mortality rates through timely intervention. By integrating genomic and clinical data, the model aims to offer personalized risk assessments, paving the way for targeted prevention and treatment strategies. The research will contribute to both computational oncology and public health, providing a scalable framework for breast cancer risk prediction in diverse populations (Aminu, 2023).
Scope and Limitations of the Study
The study is limited to the design, implementation, and evaluation of a machine learning model for predicting breast cancer susceptibility at Federal University, Birnin Kebbi, Kebbi State. It focuses exclusively on genomic and clinical data from breast cancer patients and does not extend to other cancer types.
Definitions of Terms
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Breast Cancer Susceptibility: The likelihood of developing breast cancer based on genetic and clinical risk factors.
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Machine Learning Model: A computational algorithm that learns patterns from data to make predictions or decisions.
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Genome-Wide Association Study (GWAS): A study that scans complete sets of DNA from many individuals to identify genetic variations associated with a particular disease.
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Chapter One: Introduction
