Abstract
Intelligent medical diagnosis has become common in the era of big data, although this technique has been applied to asthma only in limited contexts. Using routine blood biomarkers to identify asthma patients would make clinical diagnosis easier to implement and would enhance research of key asthma variables through data mining techniques. We used routine blood data from healthy individuals to construct a Mahalanobis space (MS). Then, we calculated Mahalanobis distances of the training routine blood data from 355 asthma patients and 1,480 healthy individuals to ensure the efficiency of MS. Orthogonal arrays and signal-to-noise ratios were used to optimize blood biomarker variables. Receiver operating characteristic (ROC) curve was used to determine the threshold value. Ultimately, we validated the system on 182 individuals based on the threshold value. Out of 35 patients with asthma, MTS correctly classified 94.15% of patients. In addition, 97.20% of 147 healthy individuals were correctly classified. The system isolated 7 routine blood biomarkers. Among these biomarkers, platelet distribution width, mean platelet volume, white blood cell count, eosinophil count, and lymphocyte ratio performed well in asthma diagnosis. In brief, MTS shows promise as an accurate method to identify asthma patients based on 7 vital blood biomarker variables and threshold determined by the ROC curve, thus offering the potential to simplify diagnostic complexity and optimize clinical efficiency.
Highlights
Asthma is a common chronic condition of the airways characterized by reversible airflow obstruction, airway hyper-responsiveness, and clinical symptoms that include wheezing, breathlessness, and chest tightness
Nine folds were used for training, and the remaining were used for testing data mining algorithms. us, there were 1,331 healthy individuals for normal training samples and 147 healthy individuals for testing samples
Our assessment of Mahalanobis–Taguchi system (MTS) to determine useful variables for predicting asthma diagnosis shows that MTS is a useful diagnostic and forecasting technique
Summary
Asthma is a common chronic condition of the airways characterized by reversible airflow obstruction, airway hyper-responsiveness, and clinical symptoms that include wheezing, breathlessness, and chest tightness. Several studies have reported the diagnosis of asthma using data mining algorithms and methods applied to intelligent diagnosis, such as support vector machine (SVM) [4, 5] and neural networks [5,6,7,8]. Platelet counts and mean platelet volume (MPV) are higher in asthmatic children than control children with no evidence of allergic disease (i.e., asthma, allergic rhinitis, or eczema), and mean MPV during an asymptomatic period is higher in individuals with exacerbated asthma than in healthy controls [11] Standardized criteria involving both assessment of risk factors and measurement of blood biomarkers that predict the risk of asthma exacerbation could provide more optimal treatment guidance and reduce healthcare costs. E purpose of this study was to apply MTS to asthma diagnosis based on assessment of routine blood data from healthy individuals and asthma patients. We compared MTS results with other algorithms to determine which had best accuracy, sensitivity, and specificity. ese results can be applied to asthma diagnosis decision systems
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