Design and Implement Artificial Intelligent Prognosis Systems for Evolving Rural Healthcare

This article aims to revolutionize rural healthcare by designing and implementing an Artificial Intelligence Prognosis System (AIPS) using Computational Intelligence and Fuzzy logic. The objective is to address the significant shortcomings in current medical diagnostics, where mis-prognosis and unnecessary treatments are prevalent, particularly in rural areas due to high diagnostic costs and a shortage of certified practitioners. The proposed AIPS emulates the cognitive functions of a real-world doctor, incorporating AI and SC techniques to learn, think, reason, and manage vagueness. Methodologically, the study integrates fuzzy logic, Programming in Logic, and membership functions to assess the severity of symptoms and calculate disease probabilities, effectively mapping the diagnostic philosophy of human doctors onto a computational model. The system's design includes production rules for various diseases, specifically focusing on tuberculosis, to validate its diagnostic capabilities. The severity of patients' symptoms is assessed using a membership function, which helps address the emotional states of patients. Additionally, a membership function is utilized to compute the prospects of various diseases, thereby enhancing the diagnostic process. Findings indicate that AIPS can significantly improve diagnostic accuracy and accessibility, particularly in underserved rural regions, by providing a cost-effective and reliable alternative to traditional medical practitioners. The newness of this work lies in its capability to mimic human-like diagnostic reasoning and emotional perception in a machine, addressing the critical healthcare disparity in rural areas and offering a scalable solution for global healthcare challenges.