An Expert System to Diagnose Pneumonia Using Fuzzy Logic

Abstract

Introduction: Pneumonia is the most common and widespread killing disease of respiratory system which is difficult to diagnose due to identical clinical signs of respiratory system. Aim: In this research, to diagnose this, a structure of a fuzzy expert system has been offered. This is done in order to help general physicians and the patients make decision and also differentiate among chronic bronchitis, tuberculosis, asthma, embolism, lung cancer. Methods: This system has been created using fuzzy expert system and it has been created in 4 stages: definition of knowledge system, design of knowledge system, implementation of system, system testing using prototype life cycle methodology. Results: The system has 97 percent sensitivity, 85 percent specificity, 93 percent accuracy to diagnose the disease. Conclusion: Framework of the knowledge of specialist physicians using fuzzy model and its rules can help diagnose the disease correctly.

Non-relational approach to developing knowledge bases of expert system prototype

“…also widely used [3]. Use of apparatus of artifi cial neural networks is the specifi c tendency in the fi eld of expert system development [7,8].…”

“…В. Гнатушенко 1 , Вік. В. Гнатушенко 2 , Н. Л. Дорош 2 , Н. О. Солодка 3 , О. А. Ляшенко 3 1 -Національний технічний університет «Дніпровська політехніка», м. Дніпро, Україна, e-mail: vvgnat@ukr.net 2 -Український державний університет науки та технологій, м. Дніпро, Україна 3 -Український державний хіміко-технологічний університет, м. Дніпро, Україна Методика. Проведено порівняння традиційного реляційного підходу й запропонованого нереляційного на прикладах формування певних запитів.…”

“…The problem topicality is substantiated by the reviews [1] describing the current research and developments in the fi eld of expert systems. Nowadays, the expert systems are used by business [2], medicine [3], agriculture [4], education [5], defense industry [6] and by other engineering applications and by many other spheres.…”

La inteligencia artificial y sus aplicaciones en medicina II: importancia actual y aplicaciones prácticas

“… Muchas investigaciones biomédicas se ven claramente beneficiadas por la IA, ya que su aplicación genera una reducción en los costes y facilita la obtención y gestión de datos a través de modelos semánticos y relaciones entre variables desde una perspectiva diferente a la de la estadística clásica. La lógica difusa se está aplicando a investigaciones dentro del ámbito de la salud y está aportando nuevas visiones en el campo de creación de modelos de expansión de enfermedades infecciosas 45 , sistemas expertos para el diagnóstico 46 o respuestas a tratamientos 47 . …”

Model of the Performance Based on Artificial Intelligence–Fuzzy Logic Description of Physical Activity

“…To date, fuzzy logic has mainly been used for pattern recognition in medical signals [ 18 ], especially with linguistic description, uncertainty and incompleteness of data. A review of the current state of knowledge shows that fuzzy logic has so far been used to diagnose diseases based on symptoms, and historical and clinical data, including among others the fields of celiac disease, diabetes, iris, heart, breast cancer, dental, cholera, brain tumor, liver, asthma, viral, Parkinson’s disease, lung, kidney, Huntington’s disease and chest diseases [ 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. The benefits of fuzzy logic are expected primarily in injuries, conditions and deficits that have not found algorithms to detect them early and accurately and need artificial intelligence support to do so [ 20 ].…”