Scopus
YÖKSİS Eşleşti
Diagnosis of urinary tract infection based on artificial intelligence methods
Computer Methods and Programs in Biomedicine · Kasım 2018
YÖKSİS Kayıtları
Diagnosis of urinary tract infection based on artificial intelligence methods
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE · 2018 SCI-Expanded
DOÇENT İLKER ALİ ÖZKAN →
Diagnosis of urinary tract infection based on artificial intelligence methods
Computer Methods and Programs in Biomedicine SCI
DOÇENT İLKER ALİ ÖZKAN →
Makale Bilgileri
DergiComputer Methods and Programs in Biomedicine
Yayın TarihiKasım 2018
Cilt / Sayfa166 · 51-59
Scopus ID2-s2.0-85054442664
Özet
Background and Objective: Urinary tract infection (UTI) is a common disease affecting the vast majority of people. UTI involves a simple infection caused by urinary tract inflammation as well as a complicated infection that may be caused by an inflammation of other urinary tract organs. Since all of these infections have similar symptoms, it is difficult to identify the cause of primary infection. Therefore, it is not easy to diagnose a UTI with routine examination procedures. Invasive methods that require surgery could be necessary. This study aims to develop an artificial intelligence model to support the diagnosis of UTI with complex symptoms. Methods: Firstly, routine examination data and definitive diagnosis results for 59 UTI patients gathered and composed as a UTI dataset. Three classification models namely; decision tree (DT), support vector machine (SVM), random forest (RF) and artificial neural network (ANN), which are widely used in medical diagnosis systems, were created to model the definitive diagnosis results using the composed UTI dataset. Accuracy, specificity and sensitivity statistical measurements were used to determine the performance of created models. Results: DT, SVM, RF and ANN models have 93.22%, 96.61%, 96.61%, 98.30% accuracy, 95.55%, 97.77%, 95.55%, 97.77% sensitivity and 85.71%, 92.85%, 100%, 100% specificy results, respectively. Conclusions: ANN has the highest accuracy result of 98.3% for UTI diagnosis within the proposed models. Although several symptoms, laboratory findings, and ultrasound results are needed for clinical UTI diagnosis, this ANN model only needs pollacuria, suprapubic pain symptoms and erythrocyte to get the same diagnosis with such accuracy. This proposed model is a successful medical decision support system for UTI with complex symptoms. Usage of this artificial intelligence method has its advantages of lower diagnosis cost, lower diagnosis time and there is no need for invasive methods.
Yazarlar (3)
1
Ilker Ali Ozkan
2
Murat Koklu
ORCID: 0000-0002-2737-2360
3
Unal Sert
Anahtar Kelimeler
Artificial intelligence methods
Artificial neural networks
Decision tree
Medical decision support systems
Random forest
Support vector machine
Urinary tract infection
Kurumlar
Necmettin Erbakan Üniversitesi
Meram Turkey
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
56
Atıf
3
Yazar
7
Anahtar Kelime