Implementation of Ensemble Machine Learning with Voting Classifier for Reliable Tuberculosis Detection Using Chest X-ray Images with Imbalance Dataset

Muhammad I Jauhari; Muhammad P.  Wirakusuma; Anka  Sidqi; I Gusti Ngurah R. A.  Putra; Inung Wijayanto; Achmad Rizal; Sugondo Hadiyoso

doi:10.35882/jeeemi.v6i4.472

Muhammad I Jauhari Telkom University
Muhammad P. Wirakusuma Telkom University
Anka Sidqi Telkom University
I Gusti Ngurah R. A. Putra Telkom University
Inung Wijayanto Telkom University
Achmad Rizal Telkom University
Sugondo Hadiyoso Telkom University

DOI: https://doi.org/10.35882/jeeemi.v6i4.472

Keywords: Tuberculosis, machine learning, ensemble learning, classifier, imbalance data

Abstract

Tuberculosis (TB) is an infectious disease caused by bacteria. Tuberculosis is spread through the air and saliva that contain mycobacterium tuberculosis. If not treated immediately, it can spread to other vital organs, such as the heart and liver, and can even lead to death. In this study, we developed a severe tuberculosis detection system using the Tuberculosis (TB) dataset with simple computation. We used 4200 data points (3500 Normal and 700 TB). In other words, this research aimed to create lightweight computation with Machine Learning (Voting Classifier in Ensemble Learning) as the classifier using Imbalance data. Initial experiments used single machine learning with the best-performing models, Support Vector Machine (SVM), and Random Forest as classifiers. With an accuracy of 98.6% and 98%, they were combined using Ensemble Learning without feature extraction; the accuracy, AUC, Recall, Precision, and F1-score using the voting classifier were 99.1%, 99.3%, 99%, 98%, and 98%, respectively.

Downloads

Download data is not yet available.

References

P. K. Das, S. Sreevatsav, and A. Abraham, “An efficient deep learning network with orthogonal softmax layer for automatic detection of tuberculosis,” Eng Appl Artif Intell, vol. 133, p. 108116, 2024, doi: https://doi.org/10.1016/j.engappai.2024.108116 [2] H. Agrawal, Pneumonia Detection Using Image Processing And Deep Learning. 2021. doi: 10.1109/ICAIS50930.2021.9395895.

V. Sharma, Nillmani, S. K. Gupta, and K. K. Shukla, “Deep learning models for tuberculosis detection and infected region visualization in chest X-ray images,” Intelligent Medicine, 2023, doi: https://doi.org/10.1016/j.imed.2023.06.001.

R. O. Panicker, K. S. Kalmady, J. Rajan, and M. K. Sabu, “Automatic detection of tuberculosis bacilli from microscopic sputum smear images using deep learning methods,” Biocybern Biomed Eng, vol. 38, no. 3, pp. 691–699, 2018, doi: https://doi.org/10.1016/j.bbe.2018.05.007.

S. K. Sharma and A. Mohan, “Tuberculosis: From an incurable scourge to a curable disease - journey over a millennium,” Indian Journal of Medical Research, vol. 137, no. 3, 2013, [Online]. Available: https://journals.lww.com/ijmr/fulltext/2013/37030/tuberculosis__from_an_incurable_scourge_to_a.7.aspx

H. Fujita, “AI-based computer-aided diagnosis (AI-CAD): the latest review to read first,” Radiol Phys Technol, vol. 13, no. 1, pp. 6–19, 2020, doi: 10.1007/s12194-019-00552-4.

X. Chen et al., “Recent advances and clinical applications of deep learning in medical image analysis,” Med Image Anal, vol. 79, p. 102444, 2022, doi: https://doi.org/10.1016/j.media.2022.102444.

A. Manconi, G. Armano, M. Gnocchi, and L. Milanesi, “A Soft-Voting Ensemble Classifier for Detecting Patients Affected by COVID-19,” Applied Sciences, vol. 12, no. 15, 2022, doi: 10.3390/app12157554.

S. Natarajan et al., “Early diagnosis and meta-agnostic model visualization of tuberculosis based on radiography images,” Sci Rep, vol. 13, Dec. 2023, doi: 10.1038/s41598-023-49195-x.

V. Chouhan et al., “A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images,” Applied Sciences, vol. 10, no. 2, 2020, doi: 10.3390/app10020559.

C. Cornelio, M. Donini, A. Loreggia, M. S. Pini, and F. Rossi, “Voting with random classifiers (VORACE): theoretical and experimental analysis,” Auton Agent Multi Agent Syst, vol. 35, no. 2, p. 22, 2021, doi: 10.1007/s10458-021-09504-y.

S. Liu, P. Reviriego, P. Montuschi, and F. Lombardi, “Error-Tolerant Computation for Voting Classifiers With Multiple Classes,” IEEE Trans Veh Technol, vol. 69, no. 11, pp. 13718–13727, 2020, doi: 10.1109/TVT.2020.3025739.

Y. Cao, T. A. Geddes, J. Y. H. Yang, and P. Yang, “Ensemble deep learning in bioinformatics,” Nat Mach Intell, vol. 2, no. 9, pp. 500–508, 2020, doi: 10.1038/s42256-020-0217-y.

H. Du, Y. Zhang, K. Gang, L. Zhang, and Y.-C. Chen, “Online ensemble learning algorithm for imbalanced data stream,” Appl Soft Comput, vol. 107, p. 107378, 2021, doi: https://doi.org/10.1016/j.asoc.2021.107378.

T. Rahman et al., “Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization,” IEEE Access, vol. 8, pp. 191586–191601, 2020, doi: 10.1109/ACCESS.2020.3031384.

H. Shahabi et al., “Flood detection and susceptibility mapping using Sentinel-1 remote sensing data and a machine learning approach: Hybrid intelligence of bagging ensemble based on K-Nearest Neighbor classifier,” Remote Sens (Basel), vol. 12, no. 2, 2020, doi: 10.3390/rs12020266.

A. Abbas, M. Abdelsamea, and M. Gaber, “DeTraC: Transfer Learning of Class Decomposed Medical Images in Convolutional Neural Networks,” IEEE Access, vol. PP, p. 1, Apr. 2020, doi: 10.1109/ACCESS.2020.2989273.

O. Awe, G. Opateye, C. Johnson, O. Tayo, and R. Dias, “Weighted Hard and Soft Voting Ensemble Machine Learning Classifiers: Application to Anaemia Diagnosis,” 2024, pp. 351–374. doi: 10.1007/978-3-031-41352-0_18.

T. N. Rincy and R. Gupta, “Ensemble Learning Techniques and its Efficiency in Machine Learning: A Survey,” in 2nd International Conference on Data, Engineering and Applications (IDEA), 2020, pp. 1–6. doi: 10.1109/IDEA49133.2020.9170675.

L. Hwangbo et al., “Stacking ensemble learning model to predict 6-month mortality in ischemic stroke patients,” Sci Rep, vol. 12, Mar. 2022, doi: 10.1038/s41598-022-22323-9.

Z. Asghari Varzaneh, M. Shanbehzadeh, and H. Kazemi-Arpanahi, “Prediction of successful aging using ensemble machine learning algorithms,” BMC Med Inform Decis Mak, vol. 22, Oct. 2022, doi: 10.1186/s12911-022-02001-6.

A. Dutta et al., “Early Prediction of Diabetes Using an Ensemble of Machine Learning Models,” Int J Environ Res Public Health, vol. 19, no. 19, 2022, doi: 10.3390/ijerph191912378.

E.-S. M. El-Kenawy, A. Ibrahim, S. Mirjalili, M. M. Eid, and S. E. Hussein, “Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images,” IEEE Access, vol. 8, pp. 179317–179335, 2020, doi: 10.1109/ACCESS.2020.3028012.