A New Diagnosing Method for Psoriasis From Exhaled Breath - Bilge Han Tozlu; Omer Faruk Akmese; Cemaleddin Simsek; Engin Senel (IEEE Access)
Psoriasis 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 Olçok 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.