Deep learning facilitates the diagnosis of adult asthma

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  • Tomita Katsuyuki
    Department of Respiratory Medicine, Yonago Medical Centre
  • Nagao Ryota
    Department of Respiratory Medicine, Yonago Medical Centre
  • Touge Hirokazu
    Department of Respiratory Medicine, Yonago Medical Centre
  • Ikeuchi Tomoyuki
    Department of Respiratory Medicine, Yonago Medical Centre
  • Sano Hiroyuki
    Department of Respiratory Medicine and Allergology, Kindai University Faculty of Medicine
  • Yamasaki Akira
    Division of Medical Oncology and Molecular Respirology, Department of Multidisciplinary Internal Medicine, School of Medicine, Tottori University Faculty of Medicine
  • Tohda Yuji
    Department of Respiratory Medicine and Allergology, Kindai University Faculty of Medicine

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<p>Background: We explored whether the use of deep learning to model combinations of symptom-physical signs and objective tests, such as lung function tests and the bronchial challenge test, would improve model performance in predicting the initial diagnosis of adult asthma when compared to the conventional machine learning diagnostic method.</p><p>Methods: The data were obtained from the clinical records on prospective study of 566 adult out-patients who visited Kindai University Hospital for the first time with complaints of non-specific respiratory symptoms. Asthma was comprehensively diagnosed by specialists based on symptom-physical signs and objective tests. Model performance metrics were compared to logistic analysis, support vector machine (SVM) learning, and the deep neural network (DNN) model.</p><p>Results: For the diagnosis of adult asthma based on symptom-physical signs alone, the accuracy of the DNN model was 0.68, whereas that for the SVM was 0.60 and for the logistic analysis was 0.65. When adult asthma was diagnosed based on symptom-physical signs, biochemical findings, lung function tests, and the bronchial challenge test, the accuracy of the DNN model increased to 0.98 and was significantly higher than the 0.82 accuracy of the SVM and the 0.94 accuracy of the logistic analysis.</p><p>Conclusions: DNN is able to better facilitate diagnosing adult asthma, compared with classical machine learnings, such as logistic analysis and SVM. The deep learning models based on symptom-physical signs and objective tests appear to improve the performance for diagnosing adult asthma.</p>

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