Design of a Quantum-Based AI Model for Enhancing Disease Prediction in Federal Medical Centre, Bauchi, Bauchi State
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Background of the Study
The integration of technology in healthcare has significantly improved diagnostic and predictive capabilities, leading to enhanced patient outcomes. With the advent of artificial intelligence (AI) and quantum computing, a new frontier has emerged in predictive medicine. The Federal Medical Centre in Bauchi is exploring innovative approaches to disease prediction to improve early diagnosis and treatment strategies. A quantum-based AI model harnesses the immense computational power of quantum computing to analyze complex biological data and detect subtle patterns indicative of disease onset (Adamu, 2023). This convergence of quantum computing and AI enables the processing of vast datasets from electronic health records, genetic information, and epidemiological data with unprecedented speed and accuracy (Jibril, 2024).
The traditional methods of disease prediction, which rely on classical computing, often face limitations in processing large volumes of data and identifying non-linear relationships between variables. In contrast, a quantum-based AI model can explore these intricate data patterns more efficiently, potentially leading to earlier detection of diseases and more personalized treatment plans. This approach is particularly pertinent for the Federal Medical Centre in Bauchi, where resource constraints and diverse patient demographics necessitate the development of robust, scalable, and highly accurate predictive models (Bassey, 2025).
Furthermore, the implementation of a quantum-based AI model in healthcare could revolutionize how medical institutions respond to emerging health challenges. By enabling real-time analysis and prediction, such a model can facilitate proactive healthcare management, reduce diagnostic errors, and ultimately improve patient care outcomes. However, the integration of quantum-based technologies in a clinical setting presents unique challenges, including high costs, the need for specialized expertise, and issues related to data privacy and security. The current study seeks to design and evaluate a quantum-based AI model tailored for disease prediction at the Federal Medical Centre, Bauchi, providing a strategic framework that addresses both technological and operational challenges while maximizing clinical benefits (Adeniyi, 2023).
Statement of the Problem
Healthcare institutions are increasingly challenged by the need to predict disease outbreaks and individual health risks accurately. At the Federal Medical Centre in Bauchi, conventional diagnostic tools and predictive models based on classical computing have shown limitations in managing and interpreting the complex and voluminous health data generated daily. These traditional systems often fall short in identifying early signs of disease, leading to delays in diagnosis and treatment, which can adversely affect patient outcomes (Ibrahim, 2023).
The inadequacies of existing disease prediction models are further compounded by the intricate nature of biological data, which requires sophisticated analysis techniques to uncover non-obvious correlations among various health indicators. The slow processing speeds and limited analytical power of classical computing frameworks hinder the development of timely and effective predictive models. A quantum-based AI model, however, offers the promise of rapid data processing and enhanced predictive accuracy by leveraging the principles of quantum computing to manage complex datasets and learn from multi-dimensional data patterns (Adamu, 2023).
Despite its potential, the implementation of a quantum-based AI model in a healthcare setting like the Federal Medical Centre faces several challenges. These include the high cost of quantum computing infrastructure, integration with existing hospital information systems, and a shortage of professionals trained in quantum technologies. Furthermore, there is limited empirical research on the deployment of quantum-based models in clinical environments, which creates uncertainty regarding their practical benefits and scalability. This study aims to address these gaps by designing a quantum-based AI model for disease prediction, thereby evaluating its feasibility, potential benefits, and the obstacles that must be overcome to ensure its successful adoption (Bassey, 2024).
Objectives of the Study
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To design a quantum-based AI model tailored for enhancing disease prediction at the Federal Medical Centre in Bauchi.
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To evaluate the predictive accuracy and operational efficiency of the proposed model compared to conventional methods.
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To identify and address the challenges associated with the integration of quantum-based AI in a clinical healthcare setting.
Research Questions
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How can a quantum-based AI model improve the accuracy and timeliness of disease prediction at the Federal Medical Centre, Bauchi?
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What are the key operational and technical challenges in integrating quantum-based AI into existing healthcare systems?
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What strategies can be adopted to overcome these challenges and ensure the scalability of the quantum-based AI model in clinical practice?
Significance of the Study
This study is significant as it explores the innovative application of quantum-based AI in enhancing disease prediction, which could lead to earlier diagnoses and more effective treatment plans at the Federal Medical Centre in Bauchi. The findings are expected to contribute to advancements in predictive healthcare, inform technology integration strategies, and ultimately improve patient care outcomes (Jibril, 2024).
Scope and Limitations of the Study
This study is limited to designing and evaluating a quantum-based AI model for disease prediction at the Federal Medical Centre, Bauchi, focusing on the specified objectives, the clinical environment in Bauchi, and selected Local Government Areas only. The investigation is confined to the outlined topic and objectives.
Definitions of Terms
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Quantum-Based AI Model: A computational framework that utilizes quantum computing principles integrated with artificial intelligence algorithms to analyze complex data sets for predictive analytics.
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Disease Prediction: The process of forecasting the likelihood of disease occurrence based on data analysis and pattern recognition.
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Federal Medical Centre: A public healthcare institution that provides diagnostic, therapeutic, and research services in a defined geographical area.
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