Open AccessMedical Diagnosis System in Healthcare Industry: A Fuzzy Approach
Author(s) -
Nina Sevani,
Adi Setiawan,
Fajar Saputra,
Richardo Kusuma Sali,
Oki Sunardi
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/852/1/012149
Subject(s) - fuzzy logic , inference , fuzzy inference , fuzzy inference system , centroid , healthcare system , computer science , inference engine , health care , fuzzy control system , artificial intelligence , data mining , medicine , adaptive neuro fuzzy inference system , economic growth , economics
Salmonella bacterial infection often cause uncertainty during medical diagnostic phase. Two most common diseases caused by salmonella bacteria are typhus and diarrhea. This study aims to apply fuzzy inference system within medical diagnostic system so that the uncertainty of diagnostic process can be minimized. At first, a knowledge-based system was developed based on physician experience, containing 13 symptoms and 11 rules. Secondly, a web-based platform was designed as a media for physician and or patient to perform diagnostic process. Thirdly, an evaluation of the proposed system was conducted by using black box testing, white box testing, and error measurement via confussion matrix. This study found that by applying triangular membership function, Mamdani inference engine, and defuzzification centroid, the system was able to differenciate between typhus and diarrhea. Furthermore, the web-based medical diagnostic system showed an error rate of 0.3. In other words, the proposed fuzzy-based system was in line with the diagnostic result proposed by the physician.