Diagnosis of Asthma Based on Routine Blood Biomarkers Using Machine Learning

Computational Intelligence and Neuroscience - Tập 2020 - Trang 1-8 - 2020
Jun Zhan1, Wen Chen2, Longsheng Cheng1, Qiong Wang2, Feifei Han2, Yubao Cui2
1School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu Province, China
2Department of Clinical Laboratory, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi 214023, Jiangsu Province, China

Tóm tắt

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.

Từ khóa


Tài liệu tham khảo

10.1007/978-1-4614-8603-9_2

10.1016/s0140-6736(16)31678-6

2013, Studies in Health Technology and Informatics, 190, 56

10.4018/ijisss.2018040102

10.1007/978-3-319-11128-5_104

10.1378/chest.10-0992

10.1186/s12890-016-0263-8

10.1016/j.jaip.2014.06.005

10.1111/pai.12381

10.1016/j.wear.2017.02.017

2000, Sankhyã Series B., 62, 233

2002

2015, Mathematics in Practice and Theory, 4, 134

10.1097/aci.0000000000000346

10.1007/s10845-008-0186-1

10.1016/j.eswa.2013.04.024

10.1016/j.jsv.2014.10.034

10.1016/j.eswa.2014.06.019

10.1016/s2212-5671(14)00231-7

10.1016/j.eswa.2012.11.014

10.1016/j.procir.2014.07.025

10.1063/1.5041558

10.1109/tkde.2007.190623

2017, Journal of Applied Statistics and Management, 36, 1059

Thông tin
Thông tin xuất bản

Computational Intelligence and Neuroscience

Tập 2020

1-8

Thông tin tác giả