TY - GEN
T1 - Deep Learning-based Decision-tree Classifier for Tuberculosis Diagnosis
AU - Lu, Zhixiang
AU - Li, Tenglong
AU - Chen, Mingming
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In recent years, medical disease-assisted diagnosis has been increasingly used. Prior to the COVID-19 epidemic, tuberculosis was the leading cause of death in the single infectious disease that dominated the global epidemic, and approximately 40% of tuberculosis patients were undiagnosed. Thus, making the development of a low-cost, non-invasive digital screening tool important for improving diagnosis in this area. In this paper, based on clinical and demographic data from 1105 patients collected from clinics in seven countries, and cough records from 1082 of these patients combined with convolutional neural networks and light gradient boosting machine to construct a model for the diagnosis of tuberculosis, with the final model achieving an AUC of 0.792 on the test set. This model is therefore a good reference for the auxiliary diagnosis of tuberculosis.
AB - In recent years, medical disease-assisted diagnosis has been increasingly used. Prior to the COVID-19 epidemic, tuberculosis was the leading cause of death in the single infectious disease that dominated the global epidemic, and approximately 40% of tuberculosis patients were undiagnosed. Thus, making the development of a low-cost, non-invasive digital screening tool important for improving diagnosis in this area. In this paper, based on clinical and demographic data from 1105 patients collected from clinics in seven countries, and cough records from 1082 of these patients combined with convolutional neural networks and light gradient boosting machine to construct a model for the diagnosis of tuberculosis, with the final model achieving an AUC of 0.792 on the test set. This model is therefore a good reference for the auxiliary diagnosis of tuberculosis.
KW - Acoustic classification
KW - component
KW - Deep learning
KW - Gradient boosting
KW - Tuberculosis diagnosis
UR - http://www.scopus.com/inward/record.url?scp=85192392304&partnerID=8YFLogxK
U2 - 10.1109/IAECST60924.2023.10502998
DO - 10.1109/IAECST60924.2023.10502998
M3 - Conference Proceeding
AN - SCOPUS:85192392304
T3 - 2023 5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023
SP - 1491
EP - 1495
BT - 2023 5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023
Y2 - 8 December 2023 through 10 December 2023
ER -
