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Non-Invasive Early Diagnosis of Obstructive Lung Diseases Leveraging Machine Learning Algorithms

Mujeeb Ur Rehman1,*, Maha Driss2,3, Abdukodir Khakimov4, Sohail Khalid1

1 Department of Electrical Engineering, Riphah International University, Islamabad, 44000, Pakistan
2 Security Engineering Lab, Prince Sultan University, Riyadh, 12435, Saudi Arabia
3 RIADI Laboratory, National School of Computer Science, University of Manouba, Manouba, 2010, Tunisia
4 Peoples’ Friendship University of Russia (RUDN University), Moscow, 117198, Russia

* Corresponding Author: Mujeeb Ur Rehman. Email: email

Computers, Materials & Continua 2022, 72(3), 5681-5697. https://doi.org/10.32604/cmc.2022.025840

Abstract

Lungs are a vital human body organ, and different Obstructive Lung Diseases (OLD) such as asthma, bronchitis, or lung cancer are caused by shortcomings within the lungs. Therefore, early diagnosis of OLD is crucial for such patients suffering from OLD since, after early diagnosis, breathing exercises and medical precautions can effectively improve their health state. A secure non-invasive early diagnosis of OLD is a primordial need, and in this context, digital image processing supported by Artificial Intelligence (AI) techniques is reliable and widely used in the medical field, especially for improving early disease diagnosis. Hence, this article presents an AI-based non-invasive and secured diagnosis for OLD using physiological and iris features. This research work implements different machine-learning-based techniques which classify various subjects, which are healthy and effective patients. The iris features include gray-level run-length matrix-based features, gray-level co-occurrence matrix, and statistical features. These features are extracted from iris images. Additionally, ten different classifiers and voting techniques, including hard and soft voting, are implemented and tested, and their performances are evaluated using several parameters, which are precision, accuracy, specificity, F-score, and sensitivity. Based on the statistical analysis, it is concluded that the proposed approach offers promising techniques for the non-invasive early diagnosis of OLD with an accuracy of 97.6%.

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APA Style
Rehman, M.U., Driss, M., Khakimov, A., Khalid, S. (2022). Non-invasive early diagnosis of obstructive lung diseases leveraging machine learning algorithms. Computers, Materials & Continua, 72(3), 5681-5697. https://doi.org/10.32604/cmc.2022.025840
Vancouver Style
Rehman MU, Driss M, Khakimov A, Khalid S. Non-invasive early diagnosis of obstructive lung diseases leveraging machine learning algorithms. Comput Mater Contin. 2022;72(3):5681-5697 https://doi.org/10.32604/cmc.2022.025840
IEEE Style
M.U. Rehman, M. Driss, A. Khakimov, and S. Khalid "Non-Invasive Early Diagnosis of Obstructive Lung Diseases Leveraging Machine Learning Algorithms," Comput. Mater. Contin., vol. 72, no. 3, pp. 5681-5697. 2022. https://doi.org/10.32604/cmc.2022.025840



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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