Scopus
YÖKSİS DOI Eşleşti
SJR Q3
Diagnosing rheumatoid arthritis disease using fuzzy expert system and machine learning techniques
Journal of Intelligent and Fuzzy Systems · Ocak 2023
YÖKSİS Kayıtları
Diagnosis Rheumatoid Arthritis Disease Using Fuzzy Expert System and Machine Learning Techniques
Journal of Intelligent & Fuzzy Systems · 2022 SCImago Journal Rank
Prof. Dr. SEMA YILMAZ →
Diagnosing rheumatoid arthritis disease using fuzzy expert system and machine learning techniques
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS · 2023 Science & Technology Collection SciVerse Scopus
Doç. Dr. İLKER ALİ ÖZKAN →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Age Group and Gender Classification Using Convolutional Neural Networks With a Fuzzy Logic-Based Filter Method for Noise Reduction
2022 ISSN: 1064-1246 SCI-Expanded Q4
Doç. Dr. AHMET CEVAHİR ÇINAR →
Investigation of type 1 and type 2 fuzzy logic controllers performance: application of speed control of BLDC motor
2022 ISSN: 1064-1246 SCI-Expanded Q4
Prof. Dr. İSMAİL SARITAŞ →
Investigation of type 1 and type 2 fuzzy logic controllers performance: application of speed control of BLDC motor
2022 ISSN: 1064-1246 SCI-Expanded Q4
Doç. Dr. ALİ YAŞAR →
Age group and gender classification using convolutional neural networks with a fuzzy logic-based filter method for noise reduction
2022 ISSN: 1064-1246 SCI Q4
Prof. Dr. ŞAKİR TAŞDEMİR →
Diagnosing rheumatoid arthritis disease using fuzzy expert system and machine learning techniques
2023 ISSN: 1064-1246 Science & Technology Collection SciVerse Scopus
Doç. Dr. İLKER ALİ ÖZKAN →
Makale Bilgileri
ISSN10641246
Yayın TarihiOcak 2023
Cilt / Sayfa44 · 5543-5557
Scopus ID2-s2.0-85161135806
Özet
Rheumatoid Arthritis (RA) is a very common autoimmune disease that causes significant morbidity and mortality, and therefore early diagnosis and treatment are important. Early diagnosis of RA and knowing the severity of the disease are very important for the treatment to be applied. The diagnosis of RA usually requires a physical examination, laboratory tests, and a review of the patient's medical history. In this study, the diagnosis of RA was made with two different methods using a fuzzy expert system (FES) and machine learning (ML) techniques, which were designed and implemented with the help of a specialist in the field, and the results were compared. For this purpose, blood counts were taken from 286 people, including 91 men and 195 women from various age groups. In the first method, an FES structure that determines the severity of RA disease has been established from blood count using the laboratory test results of CRP, ESR, RF, and ANA. The FES result that determines RA disease severity, the Anti-CCP level that is used to distinguish RA disease, and the patient's medical history were used to design the Decision Support System (DSS) that diagnoses RA disease. The DSS is web-based and publicly accessible. In the second method, RA disease was diagnosed using kNN, SVM, LR, DT, NB, and MLP algorithms, which are widely used in machine learning. To examine the effect of the patient's history on RA disease diagnosis, two different models were used in machine learning techniques, one with and one without the patient's history. The results of the fuzzy-based DSS were also compared with the diagnoses made by the specialist and the diagnoses made according to the 2010 ACR / EULAR RA classification criteria. The performed DSS has achieved a diagnostic success rate of 94.05% on 286 patients. In the study of machine learning techniques, the highest success rate was achieved with the LR model. While the success rate of the model was 91.25 % with only blood count data, the success rate was 97.90% with the addition of the patient's history. In addition to the high success rate, the results show that the patient's history is important in diagnosing RA disease.
Yazarlar (4)
1
Fatih Tarakci
ORCID: 0000-0002-7399-5999
2
Ilker Ali Ozkan
3
Sema Yılmaz
ORCID: 0000-0003-4277-3880
4
Dilek Tezcan
ORCID: 0000-0002-8295-9770
Anahtar Kelimeler
decision support system
diagnosis of disease
Fuzzy expert system
machine learning
rheumatoid arthritis
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Journal of Intelligent and Fuzzy Systems
Q3
SJR Skoru0,306
H-Index92
YayıncıSAGE Publications Ltd
ÜlkeUnited Kingdom
Artificial Intelligence (Q3)
Engineering (miscellaneous) (Q3)
Statistics and Probability (Q3)
Metrikler
7
Atıf
4
Yazar
5
Anahtar Kelime