Quick Search:

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

Rehman, Mujeeb Ur ORCID: https://orcid.org/0000-0002-4228-385X, Driss, Maha, Khakimov, Abdukodir and Khalid, Sohail (2022) Non-Invasive Early Diagnosis of Obstructive Lung Diseases Leveraging Machine Learning Algorithms. Computers, Materials & Continua, 72 (3). pp. 5681-5697.

TSP_CMC_25840.pdf - Published Version
Available under License Creative Commons Attribution.

| Preview


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%.

Keywords: Obstructive lung disease; non-invasive diagnosis; machine learning; physiological features; voting techniques

Item Type: Article
Status: Published
DOI: https://doi.org/10.32604/cmc.2022.025840
School/Department: School of Science, Technology and Health
URI: https://ray.yorksj.ac.uk/id/eprint/8163

University Staff: Request a correction | RaY Editors: Update this record