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

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.