Design of a Quantum-Based AI Model for Real-Time Disease Prediction at Federal Medical Centre, Makurdi, Benue State
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- 1-5 Chapters
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- NGN 5000
Background of the Study
The healthcare sector has seen significant advancements in data analysis, with artificial intelligence (AI) playing a pivotal role in the early detection and prediction of diseases. However, as the volume of healthcare data continues to grow, the need for faster and more accurate prediction models has become evident. Traditional AI models may struggle to process the large and complex datasets required for real-time disease prediction. Quantum computing, with its ability to perform calculations exponentially faster than classical computing, could provide an innovative solution to these challenges by enhancing the capabilities of AI models in predicting diseases in real time.
At the Federal Medical Centre in Makurdi, the application of quantum-based AI models could help predict the onset of diseases based on various factors, including patient demographics, medical history, and environmental conditions. Such a model could enable healthcare providers to deliver timely interventions, potentially saving lives and improving patient outcomes. This study will explore the design and implementation of a quantum-based AI model for real-time disease prediction, focusing on its application at the Federal Medical Centre, Makurdi.
Statement of the Problem
While AI has made significant contributions to disease prediction, the limitations of classical computing systems in processing large healthcare datasets hinder real-time predictions. Traditional models may not have the computational power required to analyze dynamic health data quickly and accurately. Quantum computing, with its potential for parallel processing, can offer faster and more accurate results. However, there is a gap in understanding how to design and implement quantum-based AI models within the healthcare context, specifically at the Federal Medical Centre in Makurdi. This study aims to address this gap by evaluating the feasibility and effectiveness of such a model.
Objectives of the Study
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To design a quantum-based AI model for real-time disease prediction at the Federal Medical Centre, Makurdi.
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To evaluate the effectiveness of quantum computing in enhancing disease prediction accuracy compared to traditional AI models.
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To explore the feasibility of implementing quantum-based AI disease prediction models in a clinical setting.
Research Questions
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How can quantum computing improve the accuracy of real-time disease prediction at the Federal Medical Centre, Makurdi?
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What factors contribute to the effectiveness of quantum-based AI models in predicting diseases?
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What are the challenges of implementing quantum-based AI models in healthcare settings?
Significance of the Study
This research could lead to the development of a cutting-edge tool for healthcare professionals to predict diseases with high accuracy and speed. The findings could revolutionize disease management at the Federal Medical Centre, Makurdi, and potentially across other healthcare facilities in Nigeria. Moreover, it could contribute to the integration of quantum computing into healthcare decision-making processes, leading to improved patient care and outcomes.
Scope and Limitations of the Study
The study will focus on the design and implementation of a quantum-based AI model for disease prediction at the Federal Medical Centre in Makurdi. Limitations include the availability of quantum computing infrastructure and the challenge of integrating quantum models into existing healthcare IT systems.
Definitions of Terms
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Quantum Computing: A computational technology that leverages quantum mechanics to perform calculations much faster than classical computers.
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Artificial Intelligence (AI): A field of computer science that focuses on creating systems capable of performing tasks that typically require human intelligence, such as learning and decision-making.
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Disease Prediction: The process of forecasting the onset or progression of diseases based on patterns found in health data.
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