Yazar "Tozlu, BH" seçeneğine göre listele

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    A New Diagnosing Method for Psoriasis From Exhaled Breath
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2025) Tozlu, BH; Akmeşe, ÖF; Şimşek, C; Şenel, E
    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 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.
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    Diagnosis of Systolic Heart Failure Disease with an Electronic Nose
    (MDPI, 2025) Yetim, M; Karavelioğlu, Y; Şimşek, C; Aydemir, Ö; Tozlu, BH
    Electronic nose technology is attracting attention with its diagnostic applications in the healthcare field. In this study, respiratory samples of individuals with systolic heart failure (HFrEF) were analyzed using an electronic nose device to investigate the diagnostic feasibility for this disease. A total of 275 breath samples were collected from 29 patients and 31 healthy volunteers followed in a cardiology clinic. Classification using support vector machines (SVM) yielded an average accuracy rate of 85.21%. The simplicity of the statistical features used in the classification, combined with the low computational complexity, increases the method's practicality. This study demonstrates that, unlike existing imaging and laboratory techniques, electronic nose technology can be considered a non-invasive, rapid, and cost-effective alternative for diagnosing heart failure, particularly notable for its potential to contribute to early diagnosis.
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    Öğe
    Feature Selection and Classification Optimization of Transformer Oil Odor Data With Recursive Feature Elimination Using Grid Search and Cross-Validation
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2025) Tozlu, BH
    Power transformers are vital for transmitting, distributing, and using energy produced in electrical energy systems. The failure of a distribution transformer can cause significant financial losses to society and cause irreparable problems. Therefore, power transformers' reliability, continuous monitoring, and fault-free operation are critical. In this study, an electronic nose system was developed to detect the duration of oil usage in power transformers based on its smell. In the system designed with eleven inexpensive gas sensors, a total of 200 transformer oil odors with four different usage periods were analyzed. Four features were selected from eighty-eight features using the Recursive Feature Elimination method with Grid Search and Cross-Validation, and they were classified with six different classifiers. With the Extra Trees algorithm, the most successful classifier, classification performance of 0.9810 CA, 0.9810 SE, 0.9937 SF was achieved without feature selection, and 0.9610 CA, 0.9610 SE, 0.9870 SF was achieved by selecting features. Dielectric breakdown voltage tests of the oils in the study were also performed, and the results supported the results of the electronic nose system. According to the results obtained, it can be concluded that transformer oil maintenance can be performed economically, practically, and reliably with the proposed system.