Development of an AI-Based System for Predicting Malaria Drug Resistance: A Case Study of Abubakar Tafawa Balewa University, Bauchi State
- Project Research
- 1-5 Chapters
- Abstract : Available
- Table of Content: Available
- Reference Style:
- Recommended for :
- NGN 5000
Background of the Study
Malaria remains one of the most prevalent infectious diseases in Nigeria, with drug resistance being one of the key factors complicating treatment efforts. The emergence of drug-resistant malaria strains has made it increasingly difficult to manage the disease, as existing treatment options become less effective. Artificial intelligence (AI) has the potential to revolutionize malaria research by enabling the development of predictive models that can identify emerging drug-resistant strains before they become widespread. Abubakar Tafawa Balewa University, Bauchi State, offers a unique opportunity to design an AI-based system that can predict malaria drug resistance by analyzing genetic and epidemiological data. By integrating machine learning algorithms with malaria genomic data, this system can provide early warnings of resistance patterns, enabling health authorities to take proactive measures to combat the spread of resistant strains.
Statement of the Problem
Malaria continues to be a major public health challenge in Nigeria, with drug resistance significantly undermining treatment efforts. While some studies have identified specific genetic mutations linked to drug resistance, there is no comprehensive, AI-driven system in Nigeria that can predict the emergence of resistance patterns in real time. The lack of such systems hinders the timely adaptation of treatment protocols and the efficient allocation of healthcare resources. Abubakar Tafawa Balewa University, Bauchi State, has the potential to develop an AI-based platform that can analyze large datasets to predict drug resistance trends and inform treatment strategies. However, the development and implementation of such a system have been hindered by the lack of computational resources and expertise in AI-based drug resistance prediction.
Objectives of the Study
-
To develop an AI-based system for predicting malaria drug resistance based on genomic and epidemiological data.
-
To evaluate the effectiveness of the AI model in predicting emerging drug-resistant strains of malaria.
-
To assess the potential of the AI system in guiding malaria treatment strategies and improving public health responses.
Research Questions
-
How can AI be applied to predict malaria drug resistance using genomic and epidemiological data?
-
What are the key factors influencing the effectiveness of the AI-based system in predicting drug resistance?
-
How can the AI-based system help improve malaria treatment strategies in Nigeria?
Significance of the Study
This research will provide an innovative AI-based tool for predicting malaria drug resistance, enabling timely interventions and improving malaria treatment in Nigeria. The findings will contribute to the global fight against drug-resistant malaria by offering a model that can be adapted in other malaria-endemic regions.
Scope and Limitations of the Study
The study will focus on developing and testing an AI-based system for predicting malaria drug resistance using data from Abubakar Tafawa Balewa University, Bauchi State. Limitations include the availability of comprehensive genomic data and the computational infrastructure required for implementing the AI model.
Definitions of Terms
-
Artificial Intelligence (AI): The use of computer systems to perform tasks that normally require human intelligence, such as learning, reasoning, and decision-making.
-
Drug Resistance: The reduction in the effectiveness of a drug in treating a disease, often due to genetic mutations in pathogens.
-
Malaria Genomic Data: Genetic information about the malaria parasite, which can be used to study its resistance to antimalarial drugs.
Related Project Materials
The Contribution of Trade Fairs to Promoting Local Industries in Wukari Local Government Area, Taraba State
Chapter One: Introduction
1.1 Background of the Study
Trade fairs serve as vital platforms for showcasing products, fostering b...
Enhancing Science Education Through AI-Powered 3D Visualizations in Maiduguri, Borno State
Background of the Study
Advancements in artificial intelligence (AI) and 3D visualization technologies are rapidly transfo...
CHALLENGES OF EXCLUSIVE BREASTFEEDING AMONG WORKING CLASS WOMEN
Background to the study
Breast feeding which is the act of feeding babies with breast milk. This is a universal cultural...
An evaluation of semantic processing in bilingual Yoruba-English speakers in Lagos
Background of the study
Semantic processing involves the cognitive mechanisms by which individuals understand, retrieve, an...
An Evaluation of Barriers to Effective Pain Management Among Stroke Patients in Benue State Healthcare Facilities
Background of the Study
Stroke is a major cause of disability and pain for many patients, yet effective...
ASSESSMENT OF HEALTH NEED FOR INTERNALLY DISPLACED PERSONS
ABSTRACT
Since the current Boko Haram insurgency started in 2009, it has killed 20,000 and displaced 2.3 million from th...
THE ROLE OF COMMUNICATION IN AN ORGANISATION PERFORMANCE
EXCERPT FROM THE STUDY
In essence, communication involves using codes that are done with the eyes, body movement or soun...
The impact of political instability on economic growth: A study of Jalingo Local Government Area, Taraba State
Background of the Study
Political instability refers to the absence of stable and predictable governance, often marked by f...
ESTIMATION OF ORGAN EQUIVALENT AND EFFECTIVE DOSES FROM DIAGNOSTIC X-RAY
ABSTRACT
The radiation dose received by the patient during the radiological examination is essential to prevent risks of...
A Critical Analysis of Business Process Reengineering in Improving Operational Efficiency: A Case Study of Logistics Companies in Taraba State
Background of the Study
Business Process Reengineering (BPR) involves the radical rede...
