Development of a Machine Learning Algorithm for Identifying Genetic Risk Factors for Cardiovascular Diseases: A Case Study of University of Maiduguri, Borno State
- Project Research
- 1-5 Chapters
- Abstract : Available
- Table of Content: Available
- Reference Style:
- Recommended for :
- NGN 5000
Background of the Study :
Cardiovascular diseases (CVDs) continue to be a leading cause of morbidity and mortality worldwide, with genetic factors playing a critical role in their development. Recent advances in machine learning have revolutionized the way complex genomic data are analyzed, enabling the identification of subtle genetic risk factors associated with CVDs. This study aims to develop a machine learning algorithm that can effectively identify genetic risk factors for cardiovascular diseases using genomic datasets. The focus on the University of Maiduguri, Borno State, is particularly relevant due to the unique genetic diversity found within this region, which is often underrepresented in global datasets (Yakubu, 2023). The algorithm will incorporate various supervised learning techniques, including decision trees, support vector machines, and deep neural networks, to detect patterns and interactions among genetic variants. Data preprocessing will involve normalization, feature selection, and dimensionality reduction to enhance the performance of the predictive models. Moreover, the algorithm will be designed to integrate with existing clinical data, allowing for the assessment of gene–environment interactions that contribute to cardiovascular risk (Abubakar, 2024). Recent studies have highlighted the potential of machine learning in accurately predicting disease risk by leveraging large-scale genomic data and sophisticated statistical models (Ibrahim, 2025). This research will further refine these techniques by focusing on the local population, addressing issues such as data imbalance and overfitting through cross-validation and ensemble methods. The ultimate goal is to provide a robust, interpretable, and scalable tool that can be used in clinical settings to identify individuals at high risk for cardiovascular diseases, thus facilitating early intervention and personalized treatment strategies. In addition, ethical considerations regarding data security, informed consent, and the responsible use of genetic information will be integral to the development process. Overall, this study seeks to bridge the gap between advanced computational techniques and practical clinical applications in the context of cardiovascular disease risk prediction.
Statement of the Problem :
Despite significant advancements in the application of machine learning to genetic data, accurately identifying genetic risk factors for cardiovascular diseases remains challenging. Many existing algorithms are developed using datasets that do not represent the genetic heterogeneity of African populations, leading to models that may not perform optimally when applied in regions like Borno State (Salihu, 2023). Furthermore, the complexity of cardiovascular diseases, which involve multifactorial interactions between genes and environmental factors, complicates the development of predictive models. Current approaches often struggle with issues of overfitting, data imbalance, and limited interpretability, which can hinder clinical adoption. Additionally, there is a scarcity of locally generated genomic data that captures the unique genetic variations present in the Nigerian population, thereby limiting the external validity of predictive algorithms. This study aims to address these challenges by developing a machine learning algorithm specifically tailored to identify genetic risk factors for CVDs in the University of Maiduguri’s local population. By incorporating advanced data preprocessing techniques and ensemble learning methods, the research seeks to improve model accuracy and robustness. The algorithm will be rigorously validated using cross-validation techniques and external testing datasets to ensure its generalizability and clinical relevance. Moreover, the study will explore the integration of clinical variables with genomic data to better capture the complex etiology of cardiovascular diseases. Addressing these issues is essential for developing a reliable predictive tool that can facilitate early diagnosis and personalized treatment plans, ultimately reducing the burden of CVDs in the region (Bello, 2024).
Objectives of the Study:
-
To develop a machine learning algorithm that identifies genetic risk factors for cardiovascular diseases.
-
To optimize data preprocessing and feature selection for improved model performance.
-
To validate the algorithm using local genomic and clinical datasets from the University of Maiduguri.
Research Questions:
-
What genetic variants are most predictive of cardiovascular diseases in the local population?
-
How can machine learning techniques be optimized to improve prediction accuracy?
-
In what ways does the integration of clinical data enhance the model’s performance?
Significance of the Study :
This study is significant because it develops a tailored machine learning algorithm to identify genetic risk factors for cardiovascular diseases in a Nigerian context. By addressing data diversity and integration challenges, the research will enhance early diagnosis and intervention strategies. The algorithm’s robust performance and interpretability can lead to personalized treatment plans, ultimately reducing the impact of cardiovascular diseases. These findings will support evidence-based healthcare decisions and contribute to the advancement of precision medicine in underrepresented populations (Ibrahim, 2025).
Scope and Limitations of the Study:
The study is limited to developing and validating a machine learning algorithm for identifying genetic risk factors for cardiovascular diseases using data from the University of Maiduguri, Borno State. It does not include prospective clinical trials or external validation beyond the local dataset.
Definitions of Terms:
-
Machine Learning Algorithm: A computational model that learns patterns from data to make predictions or decisions.
-
Genetic Risk Factors: Specific genetic variants that increase an individual’s susceptibility to a disease.
-
Cardiovascular Diseases (CVDs): A group of disorders affecting the heart and blood vessels.
Related Project Materials
An evaluation of customer perception of value in relation to promotional pricing strategies: Evidence from a beauty product in Kaduna, Nigeria
Background of the study
Promotional pricing strategies are widely used in the beauty industry to attract consumers and s...
THE ROLE OF MICRO-ORGANISMS IN WASTE RECYCLING
BACKGROUND OF THE STUDY
Microorganisms are found virtually everywhere in the natural world, where they...
The Role of IFRS in Enhancing Export Competitiveness in Nigeria
Background of the Study
International Financial Reporting Standards (IFRS) adoption has become a key dr...
Effectiveness of Broadcasting in Educating Rural Communities on COVID-19 Prevention: A Study of Dikwa Local Government Area, Borno State
Chapter One: Introduction
1.1 Background of the Study
The COVID-19 pandemic has posed significant health, social, and economic...
An appraisal of maintenance cost control mechanisms on branch profitability in banking: a case study of Stanbic IBTC Bank Nigeria
Background of the Study
In the competitive banking sector, effective cost control is essential to ensuring branch profitabi...
Evaluation of High-Performance Computing in Genomic Data Analysis: A Case Study of Usmanu Danfodiyo University, Sokoto State
Background of the Study
The advent of high-throughput sequencing technologies has led to the generation of enormous volume...
An assessment of influencer marketing strategies on consumer decision-making: Evidence from a fashion brand in Kaduna
Background of the study:
Influencer marketing has emerged as a dynamic strategy for influencing consumer decision-making by leveraging th...
The impact of gender roles on career choices among young adults in Ile-Ife Local Government Area, Osun State.
Background of the Study:
Gender roles have long influenced career choices, shaping both individual aspirations and societal...
EARTHWORM ECOLOGY, HEAVY METALS AND TOTAL PETROLEUM HYDROCARBON ASSESSMENT OF AN OIL SPILL SITE IN AGAYE, LAGOS STATE, NIGERIA
ABSTRACT
Agaye has experienced frequent spills of premium motor spirit due to pipeline vandalization. Over time,the spills have contamina...
The effect of adult education on environmental sanitation practices in Gusau Local Government Area, Zamfara State
Background of the study
Environmental sanitation is a critical factor in public health and sustainable development. In Gusa...
