Open AccessDesign of pneumonia and pulmonary tuberculosis early detection system based on adaptive neuro fuzzy inference system
Author(s) -
Mochamad Yusuf Santoso,
Am Maisarah Disrinama,
Haidar Natsir Amrullah
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1450/1/012122
Subject(s) - adaptive neuro fuzzy inference system , cluster analysis , lung , pneumonia , lung disease , medicine , inference system , inference , disease , pulmonary tuberculosis , tuberculosis , computer science , fuzzy logic , artificial intelligence , pathology , fuzzy control system
The results of Basic Health Research in 2018 showed the prevalence of pneumonia and pulmonary tuberculosis (TB) in Indonesia 4.0 percent and 0.4 percent, respectively. However, with a minimal number of lung specialists, the handling of lung disease will be too late. There are only 600-700 lung specialists in Indonesia. This amount is very less when compared with existing lung disease cases. The use of ANFIS for early detection of lung disease is growing. However, the system designed is still used for one type of disease. This research will design an expert system based on ANFIS to detect lung disease early, i.e. for pneumonia and pulmonary TB. Subtractive clustering is used for clustering process. The results of the training showed that both models were able to give better performance compared to the model built using conventional clustering methods. The test results show that both models have comparable performance compared to their counterpart.