A New Diagnosing Method for Psoriasis From Exhaled Breath

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dc.contributor.authorTozlu, BH
dc.contributor.authorAkmeşe, ÖF
dc.contributor.authorŞimşek, C
dc.contributor.authorŞenel, E
dc.date.accessioned2026-03-31T13:21:01Z
dc.date.available2026-03-31T13:21:01Z
dc.date.issued2025
dc.description.abstractPsoriasis is a chronic inflammatory skin disease with a high global prevalence. A skin biopsy is still required to diagnose the disease; no non-invasive diagnosis method has been found. It has become a popular approach for physicians as a support system, as it classifies biological data collected without human intervention in various ways with machine learning methods. Numerous studies have been conducted using machine learning methods to increase the accuracy, performance, speed, and reliability of diagnosing various diseases. This study aims to predict whether a group of patients admitted to Hitit University Erol Ol & ccedil;ok Training and Research Hospital have psoriasis based on exhaled breath measurements using an electronic nose system which was produced for this study by the authors. In total, 263 clinical records were examined; 120 (45.6%) were obtained from healthy individuals, while 143 (54.4%) belonged to psoriasis patients. In order to distinguish data from those of psoriasis patients and those of healthy individuals, six different machine learning algorithms were used on the breath data set. The best classification result was provided by the ExtraTreesClassifier algorithm, with an accuracy rate of 96.1%, while other algorithms have rates between 66.6% and 94.2%. The most important outcome of this study is that the model determined to distinguish psoriasis patients from healthy ones can also help in the early diagnosis of psoriasis.
dc.identifier.doi10.1109/ACCESS.2025.3536308
dc.identifier.issn2169-3536
dc.identifier.urihttp://dx.doi.org/10.1109/ACCESS.2025.3536308
dc.identifier.urihttps://hdl.handle.net/11491/9450
dc.identifier.volume13
dc.identifier.wosWOS:001420293500026
dc.language.isoen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartofIEEE ACCESS
dc.subjectPsoriasis
dc.subjectSensors
dc.subjectData acquisition
dc.subjectDiseases
dc.subjectGas detectors
dc.subjectMachine learning algorithms
dc.subjectElectronic noses
dc.subjectMachine learning
dc.subjectImage processing
dc.subjectAccuracy
dc.subjectPsoriasis diagnosis
dc.subjectelectronic nose
dc.subjectclassification
dc.subjectmachine learning
dc.subjectprediction
dc.titleA New Diagnosing Method for Psoriasis From Exhaled Breath
dc.typeArticle

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