Dr. Flynxz – A First Aid Mamdani-Sugeno-type fuzzy expert system for differential symptoms-based diagnosis

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 alle...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Journal of King Saud University. Computer and information sciences Ročník 34; číslo 4; s. 1138 - 1149
Hlavní autoři: Mantim Innocent Fale, Ya'u Gital Abdulsalam
Médium: Journal Article
Jazyk:angličtina
Vydáno: Springer 01.04.2022
Témata:
Decision Support System
Differential Symptoms-Based Diagnosis
Intelligent Computing
Mamdani-Sugeno-type Fuzzy Inference
ISSN:1319-1578
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
ISSN:1319-1578
DOI:10.1016/j.jksuci.2020.04.016