Deep Learning-based Decision-tree Classifier for Tuberculosis Diagnosis

Zhixiang Lu; Tenglong Li; Mingming Chen

doi:10.1109/IAECST60924.2023.10502998

Deep Learning-based Decision-tree Classifier for Tuberculosis Diagnosis

Zhixiang Lu^*
, Tenglong Li^*
, Mingming Chen

^*Corresponding author for this work

AoPHA Faculty

Xi'an Jiaotong-Liverpool University

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

5 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	2023 5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1491-1495
Number of pages	5
ISBN (Electronic)	9798350357738
DOIs	https://doi.org/10.1109/IAECST60924.2023.10502998
Publication status	Published - 2023
Event	5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023 - Hybrid, Guangzhou, China Duration: 8 Dec 2023 → 10 Dec 2023

Publication series

Name	2023 5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023

Conference

Conference	5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023
Country/Territory	China
City	Hybrid, Guangzhou
Period	8/12/23 → 10/12/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Acoustic classification
component
Deep learning
Gradient boosting
Tuberculosis diagnosis

Access to Document

10.1109/IAECST60924.2023.10502998

Cite this

Lu, Z., Li, T., & Chen, M. (2023). Deep Learning-based Decision-tree Classifier for Tuberculosis Diagnosis. In 2023 5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023 (pp. 1491-1495). (2023 5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IAECST60924.2023.10502998

Lu, Zhixiang ; Li, Tenglong ; Chen, Mingming. / Deep Learning-based Decision-tree Classifier for Tuberculosis Diagnosis. 2023 5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 1491-1495 (2023 5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023).

@inproceedings{b2f10d133d254e6399eea150010f43e5,

title = "Deep Learning-based Decision-tree Classifier for Tuberculosis Diagnosis",

abstract = "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.",

keywords = "Acoustic classification, component, Deep learning, Gradient boosting, Tuberculosis diagnosis",

author = "Zhixiang Lu and Tenglong Li and Mingming Chen",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023 ; Conference date: 08-12-2023 Through 10-12-2023",

year = "2023",

doi = "10.1109/IAECST60924.2023.10502998",

language = "English",

series = "2023 5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1491--1495",

booktitle = "2023 5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023",

}

Lu, Z, Li, T & Chen, M 2023, Deep Learning-based Decision-tree Classifier for Tuberculosis Diagnosis. in 2023 5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023. 2023 5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023, Institute of Electrical and Electronics Engineers Inc., pp. 1491-1495, 5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023, Hybrid, Guangzhou, China, 8/12/23. https://doi.org/10.1109/IAECST60924.2023.10502998

Deep Learning-based Decision-tree Classifier for Tuberculosis Diagnosis. / Lu, Zhixiang; Li, Tenglong; Chen, Mingming.
2023 5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1491-1495 (2023 5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Deep Learning-based Decision-tree Classifier for Tuberculosis Diagnosis

AU - Lu, Zhixiang

AU - Li, Tenglong

AU - Chen, Mingming

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 - https://www.scopus.com/pages/publications/85192392304

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 -

Lu Z, Li T, Chen M. Deep Learning-based Decision-tree Classifier for Tuberculosis Diagnosis. In 2023 5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 1491-1495. (2023 5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023). doi: 10.1109/IAECST60924.2023.10502998

Deep Learning-based Decision-tree Classifier for Tuberculosis Diagnosis

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this