An optimal asthma disease detection technique for voice signal using hybrid machine learning technique

Abstract Current clinical practice in the treatment of chronic respiratory diseases does not have an effective method that allows patients and caregivers to constantly monitor the severity of respiratory symptoms. This is the “asthma breath” that occurs in the respiratory tract. In this article, we propose an optimal asthma disease detection technique for voice signal using hybrid machine learning (OADD‐HML) technique. In OADD‐HML technique, we used the improved weed optimization (IWO) algorithm for asthma detection and forecasting. We analyze whether the scattered signal pattern follows the frequency of normal and abnormal breathing sounds. Second, we illustrate an enhanced hunting search (EHS) algorithm for feature extraction and selection process. Then, deep Q neural network (DQNN) classifier used to identify the asthma, crackle, and normal speech. After performing the function recovery and classification using DQNN, the respiratory number classes will be identified. The test results show several classifications of specific OADD‐HML breathing sounds, respectively, using accuracy, sensitivity, and specificity. Comparison results of asthma and non‐asthma classification of respiratory sounds suggest that the particular method works better than usual existing state‐of‐art.