Forward Chaining in Expert Systems: Application to Medical Diagnosis

Expert systems play a crucial role in modern health- care by providing accurate and automated medical diagnoses. This study explores the application of forward chaining in expert systems, specifically for diagnosing vitamin deficiencies and COVID-19 symptoms. Forward chaining, a rulebased inference method, systematically evaluates symptoms against a predefined knowledge base to generate diagnostic insights. The proposed system aims to enhance accessibility to healthcare, especially in resource-constrained settings, by reducing dependency on human experts while maintaining accuracy and consistency in diagnoses. The research outlines the design and implementation of an expert system that utilizes a dynamic rule base for medical evaluation. By integrating certainty factors, the system quantifies the confidence levels of diagnoses, improving reliability. A decision support framework is developed to analyze patient symptoms, infer possible conditions, and provide medical recommendations. Performance evaluation is conducted to assess diagnostic ac- curacy, consistency, and efficiency. The findings highlight the effectiveness of forward chaining in real-time symptom analysis, ensuring early detection of diseases and enabling proactive healthcare management. Future advancements include machine learning integration, dynamic rule updates, and natural language processing (NLP) enhancements to improve user interaction and system adaptability. Additionally, incorporating wearable health monitoring devices and cloud-based deployment can enhance scalability and real-time tracking. This study underscores the potential of AI-driven expert systems in revolutionizing medical diagnostics, offering a cost-effective and scalable solution for early disease detection and personalized treatment recommendations.