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Sheta A, Thaher T, Surani SR, Turabieh H, Braik M, Too J, Abu-El-Rub N, Mafarjah M, Chantar H, Subramanian S. Diagnosis of Obstructive Sleep Apnea Using Feature Selection, Classification Methods, and Data Grouping Based Age, Sex, and Race. Diagnostics (Basel) 2023; 13:2417. [PMID: 37510161 PMCID: PMC10377846 DOI: 10.3390/diagnostics13142417] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 07/13/2023] [Accepted: 07/15/2023] [Indexed: 07/30/2023] Open
Abstract
Obstructive sleep apnea (OSA) is a prevalent sleep disorder that affects approximately 3-7% of males and 2-5% of females. In the United States alone, 50-70 million adults suffer from various sleep disorders. OSA is characterized by recurrent episodes of breathing cessation during sleep, thereby leading to adverse effects such as daytime sleepiness, cognitive impairment, and reduced concentration. It also contributes to an increased risk of cardiovascular conditions and adversely impacts patient overall quality of life. As a result, numerous researchers have focused on developing automated detection models to identify OSA and address these limitations effectively and accurately. This study explored the potential benefits of utilizing machine learning methods based on demographic information for diagnosing the OSA syndrome. We gathered a comprehensive dataset from the Torr Sleep Center in Corpus Christi, Texas, USA. The dataset comprises 31 features, including demographic characteristics such as race, age, sex, BMI, Epworth score, M. Friedman tongue position, snoring, and more. We devised a novel process encompassing pre-processing, data grouping, feature selection, and machine learning classification methods to achieve the research objectives. The classification methods employed in this study encompass decision tree (DT), naive Bayes (NB), k-nearest neighbor (kNN), support vector machine (SVM), linear discriminant analysis (LDA), logistic regression (LR), and subspace discriminant (Ensemble) classifiers. Through rigorous experimentation, the results indicated the superior performance of the optimized kNN and SVM classifiers for accurately classifying sleep apnea. Moreover, significant enhancements in model accuracy were observed when utilizing the selected demographic variables and employing data grouping techniques. For instance, the accuracy percentage demonstrated an approximate improvement of 4.5%, 5%, and 10% with the feature selection approach when applied to the grouped data of Caucasians, females, and individuals aged 50 or below, respectively. Furthermore, a comparison with prior studies confirmed that effective data grouping and proper feature selection yielded superior performance in OSA detection when combined with an appropriate classification method. Overall, the findings of this research highlight the importance of leveraging demographic information, employing proper feature selection techniques, and utilizing optimized classification models for accurate and efficient OSA diagnosis.
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Affiliation(s)
- Alaa Sheta
- Computer Science Department, Southern Connecticut State University, New Haven, CT 06514, USA
| | - Thaer Thaher
- Department of Computer Systems Engineering, Arab American University, Jenin P.O. Box 240, Palestine
| | - Salim R Surani
- Department of Pulmonary, Critical Care & Sleep Medicine, Texas A&M University, College Station, TX 77843, USA
| | - Hamza Turabieh
- Health Management and Informatics Department, School of Medicine, University of Missouri, Columbia, MO 65212, USA
| | - Malik Braik
- Department of Computer Science, Al-Balqa Applied University, Salt 19117, Jordan
| | - Jingwei Too
- Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal 76100, Melaka, Malaysia
| | - Noor Abu-El-Rub
- Center of Medical Informatics and Enterprise Analytics, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Majdi Mafarjah
- Department of Computer Science, Birzeit University, Birzeit P.O. Box 14, Palestine
| | - Hamouda Chantar
- Faculty of Information Technology, Sebha University, Sebha 18758, Libya
| | - Shyam Subramanian
- Pulmonary, Critical Care & Sleep Medicine, Sutter Health, Tracy, CA 95376, USA
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Mafarja M, Thaher T, Al-Betar MA, Too J, Awadallah MA, Abu Doush I, Turabieh H. Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning. APPL INTELL 2023; 53:1-43. [PMID: 36785593 PMCID: PMC9909674 DOI: 10.1007/s10489-022-04427-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/23/2022] [Indexed: 02/11/2023]
Abstract
Software Fault Prediction (SFP) is an important process to detect the faulty components of the software to detect faulty classes or faulty modules early in the software development life cycle. In this paper, a machine learning framework is proposed for SFP. Initially, pre-processing and re-sampling techniques are applied to make the SFP datasets ready to be used by ML techniques. Thereafter seven classifiers are compared, namely K-Nearest Neighbors (KNN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Linear Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). The RF classifier outperforms all other classifiers in terms of eliminating irrelevant/redundant features. The performance of RF is improved further using a dimensionality reduction method called binary whale optimization algorithm (BWOA) to eliminate the irrelevant/redundant features. Finally, the performance of BWOA is enhanced by hybridizing the exploration strategies of the grey wolf optimizer (GWO) and harris hawks optimization (HHO) algorithms. The proposed method is called SBEWOA. The SFP datasets utilized are selected from the PROMISE repository using sixteen datasets for software projects with different sizes and complexity. The comparative evaluation against nine well-established feature selection methods proves that the proposed SBEWOA is able to significantly produce competitively superior results for several instances of the evaluated dataset. The algorithms' performance is compared in terms of accuracy, the number of features, and fitness function. This is also proved by the 2-tailed P-values of the Wilcoxon signed ranks statistical test used. In conclusion, the proposed method is an efficient alternative ML method for SFP that can be used for similar problems in the software engineering domain.
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Affiliation(s)
- Majdi Mafarja
- Department of Computer Science, Birzeit University, Birzeit, Palestine
| | - Thaer Thaher
- Department of Computer Systems Engineering, Arab American University, Jenin, Palestine
- Information Technology Engineering, Al-Quds University, Abu Dies, Jerusalem, Palestine
| | - Mohammed Azmi Al-Betar
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab EmiratesDeepSinghML2017, Irbid, Jordan
| | - Jingwei Too
- Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal Melaka, Malaysia
| | - Mohammed A. Awadallah
- Department of Computer Science, Al-Aqsa University, P.O. Box 4051, Gaza, Palestine
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, United Arab Emirates
| | - Iyad Abu Doush
- Department of Computing, College of Engineering and Applied Sciences, American University of Kuwait, Salmiya, Kuwait
- Computer Science Department, Yarmouk University, Irbid, Jordan
| | - Hamza Turabieh
- Department of Health Management and Informatics, University of Missouri, Columbia, 5 Hospital Drive, Columbia, MO 65212 USA
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