Dr. Flynxz – A First Aid Mamdani-Sugeno-type fuzzy expert system for differential symptoms-based diagnosis - Mantim Innocent Fale; Ya'u Gital Abdulsalam (Journal of King Saud University. Computer and information sciences, 2022)
When we fall sick, we seek professional medical assistance if situations warrant. Medical diagnostic processes are usually based on signs, symptoms and laboratory investigations. Some patients respond to treatment while others react (or are allergic) to treatment. Substances or drugs that cause allergies are called Allergens. Allergies, when not managed, can be chaotic to human health. Considering these scenarios and a lot of others, an expert system (ES) for differential symptoms-based diagnosis and drug allergy management comes in handy. This paper implements ‘Dr. Flynxz – A First Aid Mamdani-Sugeno-type Fuzzy Expert System for Differential Symptoms-Based Diagnosis’. It also introduced three novel algorithms that were used to implement Dr. Flynxz’s Ailment Prediction (Forward Chaining Approach and Backward Chaining Approach) and Allergy Management mechanisms. A comparison of the proposed FMI Ailments Prediction Algorithm with ada 2.37.2 (a rule based approach) and fuzzy logic both confirm Dr. Flynxz’s prediction. Experimental results indicate performance improvement in terms of prediction accuracy. The implemented system can be useful in the area of medical diagnosis and allergy management while the proposed algorithms will be useful in the development of Mamdani-Sugeno-type fuzzy inference systems.