2020
DOI: 10.22219/kinetik.v5i1.996
|View full text |Cite
|
Sign up to set email alerts
|

An application of ANFIS for Lung Diseases Early Detection System

Abstract: Indonesian Basic Health Research in 2018 showed the prevalence of pneumonia, pulmonary tuberculosis (TB) and lung cancer in Indonesia 4.0% 0.4% and 0.18%, respectively. However, the number of lung specialists is small. According to the Indonesian Lung Specialist Association webpage, the number of doctors joined in the association up to 2008 were 452. This amount is very less when compared with existing lung disease cases. Thus, the handling of lung disease will be too late. The use of ANFIS for early detection… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0
1

Year Published

2022
2022
2023
2023

Publication Types

Select...
3

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(2 citation statements)
references
References 20 publications
0
1
0
1
Order By: Relevance
“…Adaptive Neuro-Fuzzy Inference System (ANFIS) is an adaptive network class that is functionally equivalent to the Fuzzy Inference System. ANFIS control model in the form of TSK (Takagi Sugeno) which has the simplicity of calculations [25]. This control utilizes fuzzy capabilities in reasoning and neural networks in learning [26].…”
Section: Design Of Anfis Controllermentioning
confidence: 99%
“…Adaptive Neuro-Fuzzy Inference System (ANFIS) is an adaptive network class that is functionally equivalent to the Fuzzy Inference System. ANFIS control model in the form of TSK (Takagi Sugeno) which has the simplicity of calculations [25]. This control utilizes fuzzy capabilities in reasoning and neural networks in learning [26].…”
Section: Design Of Anfis Controllermentioning
confidence: 99%
“…(2020) untuk mendeteksi dini penyakit pada paru-paru. Hasil pada penelitian tersebut berbentuk aplikasi dimana hasil uji coba data training menghasilkan akurasi 94% sedangkan uji coba data testing menghasilkan akurasi sebesar 100% (Santoso et al, 2020). Kour, dkk.…”
unclassified