1 Automation and Process Control Engineer, Gist Limited, United Kingdom.
2 Software/Machine Learning Specialist, Morgan State University, Baltimore, Md, USA.
3 Public Health Specialist, United Kingdom.
4 Microbiologist, University of Lagos, Nigeria.
5 System Engineer, University of Lagos, Nigeria.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(03), 063-081
Received on 16 July 2024; revised on 29 August 2024; accepted on 31 August 2024
Neural systems are inherently complex and susceptible to dysfunction due to various stressors, leading to significant morbidity. This article presents a novel MATLAB-based framework that leverages artificial intelligence (AI) and machine learning techniques for predictive stress analysis and morbidity assessment in neural systems. By integrating deep learning models, particularly convolutional neural networks (CNNs), the framework is designed to detect early signs of neural dysfunction with high accuracy. The study utilizes a comprehensive dataset, applying advanced preprocessing methods to optimize model performance. Key findings demonstrate that the AI-driven approach outperforms traditional methods in both predictive accuracy and the early detection of morbidity risks. The MATLAB implementation is detailed, highlighting the practical applications of the framework in real-world scenarios. This work not only advances the field of neural system analysis but also underscores the transformative potential of AI and machine learning in enhancing diagnostic precision and preventive care. The article concludes by discussing the implications of these findings for clinical practice and future research, particularly in improving patient outcomes through early intervention.
AI; Machine learning; Predictive stress analysis; Morbidity assessment; Neural dysfunction; MATLAB
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Joseph Nnaemeka Chukwunweike, Caleb Kadiri, Akudo Sylveria Williams, Akinsuyi Oluwamayowa and Akinsuyi Samson. Applying AI and machine learning for predictive stress analysis and morbidity assessment in neural systems: A MATLAB-based framework for detecting and addressing neural dysfunction. World Journal of Advanced Research and Reviews, 2024, 23(3), 063-081. Article DOI: https://doi.org/10.30574/wjarr.2024.23.3.2645