Comparative performance of ensemble models in predicting dental provider types: insights from fee-for-service data
DOI:
https://doi.org/10.56294/dm2025750Keywords:
Machine learning, dental provider classification, ensemble models, healthcare analytics, safety net clinicsAbstract
Dental provider classification plays a crucial role in optimizing healthcare resource allocation and policy planning. Effective categorization of providers, such as standard rendering providers and safety net clinic (SNC) providers, enhances service delivery to underserved populations. To evaluate the performance of machine learning models in classifying dental providers using a 2018 dataset. A dataset of 24,300 instances with 20 features was analyzed, including beneficiary and service counts across fee-for-service (FFS), Geographic Managed Care, and Pre-Paid Health Plans. Providers were categorized by delivery system and patient age groups (0–20 and 21+). Despite 38.1% missing data, multiple machine learning algorithms were tested, including k-Nearest Neighbors (kNN), Decision Trees, Support Vector Machines (SVM), Stochastic Gradient Descent (SGD), Random Forest, Neural Networks, and Gradient Boosting. A 10-fold cross-validation approach was applied, and models were evaluated using AUC, classification accuracy (CA), F1-score, precision, and recall. Neural Networks achieved the highest AUC (0.975) and CA (94.1%), followed by Random Forest (AUC: 0.948, CA: 93.0%). These models effectively handled imbalanced data and complex feature interactions, outperforming traditional classifiers like Logistic Regression and SVM. Advanced machine learning techniques, particularly ensemble and deep learning models, significantly enhance dental workforce classification. Their integration into healthcare analytics can improve provider identification and resource distribution, benefiting underserved populations.
References
1. Wahed MA, Alqaraleh M, Alzboon MS, Al-Batah MS. Application of Artificial Intelligence for Diagnosing Tumors in the Female Reproductive System: A Systematic Review. Multidiscip. 2025;3:54.
2. Alqaraleh M, Al-Batah M, Salem Alzboon M, Alzaghoul E. Automated quantification of vesicoureteral reflux using machine learning with advancing diagnostic precision. Data Metadata. 2025;4:460. DOI: https://doi.org/10.56294/dm2025460
3. Salem Alzboon M, Subhi Al-Batah M, Alqaraleh M, Alzboon F, Alzboon L. Guardians of the Web: Harnessing Machine Learning to Combat Phishing Attacks. Gamification Augment Real [Internet]. 2025 Jan;3:91. Available from: http://dx.doi.org/10.56294/gr202591 DOI: https://doi.org/10.56294/gr202591
4. Alqaraleh M, Salem Alzboon M, Subhi Al-Batah M, Solayman Migdadi H. From Complexity to Clarity: Improving Microarray Classification with Correlation-Based Feature Selection. LatIA [Internet]. 2025 Jan 1;3:84. Available from: https://latia.ageditor.uy/index.php/latia/article/view/84 DOI: https://doi.org/10.62486/latia202584
5. Alzboon MS, Subhi Al-Batah M, Alqaraleh M, Alzboon F, Alzboon L. Phishing Website Detection Using Machine Learning. Gamification Augment Real [Internet]. 2025 Jan 16;3:81. Available from: http://dx.doi.org/10.56294/gr202581 DOI: https://doi.org/10.56294/gr202581
6. Alqaraleh M, Salem Alzboon M, Mohammad SA-B. Optimizing Resource Discovery in Grid Computing: A Hierarchical and Weighted Approach with Behavioral Modeling. LatIA [Internet]. 2025 Jan 1;3:97. Available from: https://latia.ageditor.uy/index.php/latia/article/view/97 DOI: https://doi.org/10.62486/latia202597
7. Alqaraleh M, Salem Alzboon M, Subhi Al-Batah M. Real-Time UAV Recognition Through Advanced Machine Learning for Enhanced Military Surveillance. Gamification Augment Real [Internet]. 2025 Jan 1;3:63. Available from: https://gr.ageditor.ar/index.php/gr/article/view/63 DOI: https://doi.org/10.56294/gr202563
8. Wahed MA, Alqaraleh M, Alzboon MS, Subhi Al-Batah M. AI Rx: Revolutionizing Healthcare Through Intelligence, Innovation, and Ethics. Semin Med Writ Educ [Internet]. 2025 Jan 1;4:35. Available from: https://mw.ageditor.ar/index.php/mw/article/view/35 DOI: https://doi.org/10.56294/mw202535
9. Abdel Wahed M, Alqaraleh M, Salem Alzboon M, Subhi Al-Batah M. Application of Artificial Intelligence for Diagnosing Tumors in the Female Reproductive System: A Systematic Review. Multidiscip [Internet]. 2025 Jan 1;3:54. Available from: https://multidisciplinar.ageditor.uy/index.php/multidisciplinar/article/view/54 DOI: https://doi.org/10.62486/agmu202554
10. Wahed MA, Alqaraleh M, Salem Alzboon M, Subhi Al-Batah M. Evaluating AI and Machine Learning Models in Breast Cancer Detection: A Review of Convolutional Neural Networks (CNN) and Global Research Trends. LatIA [Internet]. 2025 Jan 1;3:117. Available from: https://latia.ageditor.uy/index.php/latia/article/view/117 DOI: https://doi.org/10.62486/latia2025117
11. Abuashour A, Salem Alzboon M, Kamel Alqaraleh M, Abuashour A. Comparative Study of Classification Mechanisms of Machine Learning on Multiple Data Mining Tool Kits. Am J Biomed Sci Res 2024 [Internet]. 2024;22(1):1. Available from: www.biomedgrid.com DOI: https://doi.org/10.34297/AJBSR.2024.22.002913
12. Mowafaq SA, Alqaraleh M, Al-Batah MS. AI in the Sky: Developing Real-Time UAV Recognition Systems to Enhance Military Security. Data Metadata. 2024;3:417. DOI: https://doi.org/10.56294/dm2024.417
13. Alqaraleh M, Alzboon MS, Al-Batah MS. Skywatch: Advanced Machine Learning Techniques for Distinguishing UAVs from Birds in Airspace Security. Int J Adv Comput Sci Appl [Internet]. 2024;15(11). Available from: http://dx.doi.org/10.14569/IJACSA.2024.01511104 DOI: https://doi.org/10.14569/IJACSA.2024.01511104
14. Wahed MA, Alzboon MS, Alqaraleh M, Al-Batah M, Bader AF, Wahed SA. Enhancing Diagnostic Precision in Pediatric Urology: Machine Learning Models for Automated Grading of Vesicoureteral Reflux. In: 2024 7th International Conference on Internet Applications, Protocols, and Services (NETAPPS) [Internet]. IEEE; 2024. p. 1–7. Available from: http://dx.doi.org/10.1109/netapps63333.2024.10823509 DOI: https://doi.org/10.1109/NETAPPS63333.2024.10823509
15. Abdel Wahed M, Al-Batah M, Salem Alzboon M, Fuad Bader A, Alqaraleh M. Technological Innovations in Autonomous Vehicles: A Focus on Sensor Fusion and Environmental Perception [Internet]. 2024 7th International Conference on Internet Applications, Protocols, and Services (NETAPPS). IEEE; 2024 Nov. Available from: http://dx.doi.org/10.1109/netapps63333.2024.10823624 DOI: https://doi.org/10.1109/NETAPPS63333.2024.10823624
16. Alzboon MS, Alqaraleh M, Wahed MA, Alourani A, Bader AF, Al-Batah M. AI-Driven UAV Distinction: Leveraging Advanced Machine Learning. In: 2024 7th International Conference on Internet Applications, Protocols, and Services (NETAPPS) [Internet]. IEEE; 2024. p. 1–7. Available from: http://dx.doi.org/10.1109/netapps63333.2024.10823488 DOI: https://doi.org/10.1109/NETAPPS63333.2024.10823488
17. Wahed MA, Alzboon MS, Alqaraleh M, Halasa A, Al-Batah M, Bader AF. Comprehensive Assessment of Cybersecurity Measures: Evaluating Incident Response, AI Integration, and Emerging Threats. In: 2024 7th International Conference on Internet Applications, Protocols, and Services (NETAPPS) [Internet]. IEEE; 2024. p. 1–8. Available from: http://dx.doi.org/10.1109/netapps63333.2024.10823603 DOI: https://doi.org/10.1109/NETAPPS63333.2024.10823603
18. Alzboon MS, Al-Shorman HM, Alka’awneh SMN, Saatchi SG, Alqaraleh MKS, Samara EIM, et al. The Role of Perceived Trust in Embracing Artificial Intelligence Technologies: Insights from Jordan’s SME Sector. In: Studies in Computational Intelligence [Internet]. Springer Nature Switzerland; 2024. p. 1–15. Available from: http://dx.doi.org/10.1007/978-3-031-74220-0_1 DOI: https://doi.org/10.1007/978-3-031-74220-0_1
19. Wahed MA, Alzboon MS, Alqaraleh M, Ayman J, Al-Batah M, Bader AF. Automating Web Data Collection: Challenges, Solutions, and Python-Based Strategies for Effective Web Scraping. In: 2024 7th International Conference on Internet Applications, Protocols, and Services, NETAPPS 2024 [Internet]. IEEE; 2024. p. 1–6. Available from: http://dx.doi.org/10.1109/netapps63333.2024.10823528 DOI: https://doi.org/10.1109/NETAPPS63333.2024.10823528
20. Al-Batah M, Salem Alzboon M, Alqaraleh M, Ahmad Alzaghoul F. Comparative Analysis of Advanced Data Mining Methods for Enhancing Medical Diagnosis and Prognosis. Data Metadata. 2024;3:465. DOI: https://doi.org/10.56294/dm2024.465
21. Alqaraleh M. Enhancing Internet-based Resource Discovery: The Efficacy of Distributed Quadtree Overlay. In: Proceedings of the 3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024. 2024. p. 1619–28. DOI: https://doi.org/10.1109/ICAAIC60222.2024.10575078
22. Alqaraleh M. Enhanced Resource Discovery Algorithm for Efficient Grid Computing. In: Proceedings of the 3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024. 2024. p. 925–31. DOI: https://doi.org/10.1109/ICAAIC60222.2024.10575479
23. Al-Batah MS, Salem Alzboon M, Solayman Migdadi H, Alkhasawneh M, Alqaraleh M. Advanced Landslide Detection Using Machine Learning and Remote Sensing Data. Data Metadata [Internet]. 2024 Oct 7;1. Available from: https://dm.ageditor.ar/index.php/dm/article/view/419/782 DOI: https://doi.org/10.56294/dm2024.419
24. Al-Shanableh N, Alzyoud M, Al-Husban RY, Alshanableh NM, Al-Oun A, Al-Batah MS, et al. Advanced ensemble machine learning techniques for optimizing diabetes mellitus prognostication: A detailed examination of hospital data. Data Metadata. 2024;3:363. DOI: https://doi.org/10.56294/dm2024.363
25. Al-Batah MS, Alzboon MS, Alzyoud M, Al-Shanableh N. Enhancing Image Cryptography Performance with Block Left Rotation Operations. Appl Comput Intell Soft Comput. 2024;2024(1):3641927. DOI: https://doi.org/10.1155/2024/3641927
26. Alqaraleh M, Alzboon MS, Al-Batah MS, Wahed MA, Abuashour A, Alsmadi FH. Harnessing Machine Learning for Quantifying Vesicoureteral Reflux: A Promising Approach for Objective Assessment. Int J online Biomed Eng. 2024;20(11):123–45. DOI: https://doi.org/10.3991/ijoe.v20i11.49673
27. Abdalla-Aslan R, Yeshua T, Kabla D, Leichter I, Nadler C. An artificial intelligence system using machine-learning for automatic detection and classification of dental restorations in panoramic radiography. Oral Surg Oral Med Oral Pathol Oral Radiol. 2020;130(5):593–602. DOI: https://doi.org/10.1016/j.oooo.2020.05.012
28. Lubbad MAH, Kurtulus IL, Karaboga D, Kilic K, Basturk A, Akay B, et al. A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System. J Imaging Informatics Med. 2024; DOI: https://doi.org/10.1007/s10278-024-01086-x
29. Odeh S, Samara A, Zreineh N, Obeid S, Ibrieghieth M, Obaid M. Advancements in Dental Diagnostics: YOLOv3-Powered Machine Learning for Automated Teeth Problem Detection. In: 2023 24th International Arab Conference on Information Technology, ACIT 2023. 2023. DOI: https://doi.org/10.1109/ACIT58888.2023.10453865
30. Ogwo C, Levy S, Warren J, Caplan D, Brown G. Trajectories of Dental Caries From Childhood to Young Adulthood: Unsupervised Machine Learning Approach. Res Sq [Internet]. 2023; Available from: http://www.ncbi.nlm.nih.gov/pubmed/37546769%0Ahttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC10402208 DOI: https://doi.org/10.21203/rs.3.rs-3125821/v1
31. Atalan A, Şahin H. Forecasting of the Dental Workforce with Machine Learning Models. Mühendislik Bilim ve Araştırmaları Derg. 2024;6(1):125–32. DOI: https://doi.org/10.46387/bjesr.1455345
32. Mahanayake KMLK, Nawarathna LS, Arambawatta AKS, Abeysundara RGAP. A Machine Learning Approach for Determination of Gender of Sri Lankan People by Using the Dental Arch Dimensions. In: ICARC 2024 - 4th International Conference on Advanced Research in Computing: Smart and Innovative Trends in Next Generation Computing Technologies. 2024. p. 1–6. DOI: https://doi.org/10.1109/ICARC61713.2024.10499749
33. Haider M. Developing an AI-Powered Algorithm for Automated Detection and Classification of Dental Caries from Intraoral Radiographs: A Machine Learning Approach. J Pharm Bioallied Sci [Internet]. 2024;16(Suppl 4):S3089–91. Available from: http://www.ncbi.nlm.nih.gov/pubmed/39926973%0Ahttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC11805296
34. Nantakeeratipat T, Apisaksirikul N, Boonrojsaree B, Boonkijkullatat S, Simaphichet A. Automated machine learning for image-based detection of dental plaque on permanent teeth. Front Dent Med. 2024;5. DOI: https://doi.org/10.3389/fdmed.2024.1507705
35. Filho JMM, da Silva Ferreira MA, Monteiro DLA, de Nazaré Wanderley Lira G, de Araújo TP, de Moraes Ramalho AKB, et al. Model Proposal for Equity Production in Access to Dental Specialty Centers. Pesqui Bras Odontopediatria Clin Integr. 2024;24. DOI: https://doi.org/10.1590/pboci.2024.061
36. Guo YX, Bu WQ, Tang Y, Wu D, Yang H, Meng HT, et al. Dental Age Estimation in Northern Chinese Han Children and Adolescents Using Demirjian’s Method Combined with Machine Learning Algorithms. J Forensic Med. 2024;40(2):135–42.
37. Anusha N, Ashwini B, Venugopala PS. Study on Detecting the Teeth and Classifying the Teeth Structure using Machine Learning and CNN. In: 2024 IEEE 9th International Conference for Convergence in Technology, I2CT 2024. 2024.
38. Ramos-Gomez F, Marcus M, Maida CA, Wang Y, Kinsler JJ, Xiong D, et al. Using a machine learning algorithm to predict the likelihood of presence of dental caries among children aged 2 to 7. Dent J. 2021;9(12). DOI: https://doi.org/10.3390/dj9120141
39. SURYAWANSHI A, BEHERA N. Application of Machine Learning For Prediction Dental Material Wear. J Polym Mater. 2024;40(3–4):305–16. DOI: https://doi.org/10.32381/JPM.2023.40.3-4.11
40. Xiong D, Marcus M, Maida CA, Lyu Y, Hays RD, Wang Y, et al. Development of short forms for screening children’s dental caries and urgent treatment needs using item response theory and machine learning methods. PLoS One. 2024;19(3 March). DOI: https://doi.org/10.1371/journal.pone.0299947
41. Khan MK. Novel applications of artificial intelligence, machine learning, and deep learning-based modalities in dental traumatology: An overview of evidence-based literature. MRIMS J Heal Sci. 2024; DOI: https://doi.org/10.4103/mjhs.mjhs_37_24
42. Muhyeeddin A, Mowafaq SA, Al-Batah MS, Mutaz AW. Advancing Medical Image Analysis: The Role of Adaptive Optimization Techniques in Enhancing COVID-19 Detection, Lung Infection, and Tumor Segmentation. LatIA [Internet]. 2024 Sep 29;2(74):74. Available from: https://latia.ageditor.uy/index.php/latia/article/view/74 DOI: https://doi.org/10.62486/latia202474
43. Putri AK, Alzboon MS. Doctor Adam Talib’s Public Relations Strategy in Improving the Quality of Patient Service. Sinergi Int J Commun Sci. 2023;1(1):42–54. DOI: https://doi.org/10.61194/ijcs.v1i1.19
44. Al-Batah MS, Alzboon MS, Alazaidah R. Intelligent Heart Disease Prediction System with Applications in Jordanian Hospitals. Int J Adv Comput Sci Appl. 2023;14(9):508–17. DOI: https://doi.org/10.14569/IJACSA.2023.0140954
45. Alzboon MS, Al-Batah MS. Prostate Cancer Detection and Analysis using Advanced Machine Learning. Int J Adv Comput Sci Appl. 2023;14(8):388–96. DOI: https://doi.org/10.14569/IJACSA.2023.0140843
46. Alzboon MS, Bader AF, Abuashour A, Alqaraleh MK, Zaqaibeh B, Al-Batah M. The Two Sides of AI in Cybersecurity: Opportunities and Challenges. In: Proceedings of 2023 2nd International Conference on Intelligent Computing and Next Generation Networks, ICNGN 2023. 2023. DOI: https://doi.org/10.1109/ICNGN59831.2023.10396670
47. Alzboon MS, Al-Batah M, Alqaraleh M, Abuashour A, Bader AF. A Comparative Study of Machine Learning Techniques for Early Prediction of Diabetes. In: 2023 IEEE 10th International Conference on Communications and Networking, ComNet 2023 - Proceedings. 2023. p. 1–12. DOI: https://doi.org/10.1109/ComNet60156.2023.10366688
48. Alzboon MS, Al-Batah M, Alqaraleh M, Abuashour A, Bader AF. A Comparative Study of Machine Learning Techniques for Early Prediction of Prostate Cancer. In: 2023 IEEE 10th International Conference on Communications and Networking, ComNet 2023 - Proceedings. 2023. p. 1–12. DOI: https://doi.org/10.1109/ComNet60156.2023.10366703
49. Alzboon MS, Qawasmeh S, Alqaraleh M, Abuashour A, Bader AF, Al-Batah M. Machine Learning Classification Algorithms for Accurate Breast Cancer Diagnosis. In: 2023 3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023. 2023. DOI: https://doi.org/10.1109/eSmarTA59349.2023.10293415
50. Alzboon MS, Al-Batah MS, Alqaraleh M, Abuashour A, Bader AFH. Early Diagnosis of Diabetes: A Comparison of Machine Learning Methods. Int J online Biomed Eng. 2023;19(15):144–65. DOI: https://doi.org/10.3991/ijoe.v19i15.42417
51. Alzboon MS, Qawasmeh S, Alqaraleh M, Abuashour A, Bader AF, Al-Batah M. Pushing the Envelope: Investigating the Potential and Limitations of ChatGPT and Artificial Intelligence in Advancing Computer Science Research. In: 2023 3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023. 2023. DOI: https://doi.org/10.1109/eSmarTA59349.2023.10293294
52. Alzboon MS. Survey on Patient Health Monitoring System Based on Internet of Things. Inf Sci Lett. 2022;11(4):1183–90. DOI: https://doi.org/10.18576/isl/110418
53. Alzboon M. Semantic Text Analysis on Social Networks and Data Processing: Review and Future Directions. Inf Sci Lett. 2022;11(5):1371–84. DOI: https://doi.org/10.18576/isl/110506
54. Alzboon MS, Aljarrah E, Alqaraleh M, Alomari SA. Nodexl Tool for Social Network Analysis. Turkish J Comput Math Educ. 2021;12(14):202–16.
55. Alomari SA, Salaimeh S Al, Jarrah E Al, Alzboon MS. Enhanced logistics information service systems performance: using theoretical model and cybernetics’ principles. WSEAS Trans Bus Econ [Internet]. 2020 Apr;17:278–87. Available from: https://wseas.com/journals/bae/2020/a585107-896.pdf DOI: https://doi.org/10.37394/23207.2020.17.29
56. Alomari SA, Alzboon MS, Al-Batah MS, Zaqaibeh B. A novel adaptive schema to facilitates playback switching technique for video delivery in dense LTE cellular heterogeneous network environments. Int J Electr Comput Eng [Internet]. 2020 Oct;10(5):5347. Available from: http://ijece.iaescore.com/index.php/IJECE/article/view/16563 DOI: https://doi.org/10.11591/ijece.v10i5.pp5347-5367
57. Alomari SA, Alqaraleh M, Aljarrah E, Alzboon MS. Toward achieving self-resource discovery in distributed systems based on distributed quadtree. J Theor Appl Inf Technol. 2020;98(20):3088–99.
58. Alzboon MS, Mahmuddin M, Arif S. Resource discovery mechanisms in shared computing infrastructure: A survey. In: Advances in Intelligent Systems and Computing. 2020. p. 545–56. DOI: https://doi.org/10.1007/978-3-030-33582-3_51
59. Shawawreh S, Alomari SA, Alzboon MS, Al Salaimeh S. Evaluation of knowledge quality in the E -learning system. Int J Eng Res Technol. 2019;12(4):548–53.
60. Alomari, Alzboon, Zaqaibeh, Al-Batah, Saleh Ali, Mowafaq Salem, Belal MS. An Effective Self-Adaptive Policy for Optimal Video Quality over Heterogeneous Mobile Devices and Network Discovery Services. Appl Math Inf Sci [Internet]. 2019 May;13(3):489–505. Available from: http://www.naturalspublishing.com/Article.asp?ArtcID=19739 DOI: https://doi.org/10.18576/amis/130322
61. Banikhalaf M, Alomari SA, Alzboon MS. An advanced emergency warning message scheme based on vehicles speed and traffic densities. Int J Adv Comput Sci Appl. 2019;10(5):201–5. DOI: https://doi.org/10.14569/IJACSA.2019.0100526
62. Alzboon MS. Internet of things between reality or a wishing - list : a survey. Int J Eng & Technol. 2019;7(June):956–61.
63. Al-Batah M, Zaqaibeh B, Alomari SA, Alzboon MS. Gene Microarray Cancer classification using correlation based feature selection algorithm and rules classifiers. Int J online Biomed Eng. 2019;15(8):62–73. DOI: https://doi.org/10.3991/ijoe.v15i08.10617
64. Al Tal S, Al Salaimeh S, Ali Alomari S, Alqaraleh M. The modern hosting computing systems for small and medium businesses. Acad Entrep J. 2019;25(4):1–7.
65. Alzboon MS, Alomari S, Al-Batah MS, Alomari SA, Banikhalaf M. The characteristics of the green internet of things and big data in building safer, smarter, and sustainable cities Vehicle Detection and Tracking for Aerial Surveillance Videos View project Evaluation of Knowledge Quality in the E-Learning System View pr [Internet]. Vol. 6, Article in International Journal of Engineering and Technology. 2017. p. 83–92. Available from: https://www.researchgate.net/publication/333808921
66. Arif S, Alzboon MS, Mahmuddin M. Distributed quadtree overlay for resource discovery in shared computing infrastructure. Adv Sci Lett. 2017;23(6):5397–401. DOI: https://doi.org/10.1166/asl.2017.7384
67. Mahmuddin M, Alzboon MS, Arif S. Dynamic network topology for resource discovery in shared computing infrastructure. Adv Sci Lett. 2017;23(6):5402–5. DOI: https://doi.org/10.1166/asl.2017.7385
68. Mowafaq Salem Alzboon M. Mahmuddin ASCA. Challenges and Mitigation Techniques of Grid Resource Management System. In: National Workshop on FUTURE INTERNET RESEARCH (FIRES2016). 2016. p. 1–6.
69. Al-Oqily I, Alzboon M, Al-Shemery H, Alsarhan A. Towards autonomic overlay self-load balancing. In: 2013 10th International Multi-Conference on Systems, Signals and Devices, SSD 2013. Ieee; 2013. p. 1–6. DOI: https://doi.org/10.1109/SSD.2013.6564018
70. Alzboon MS, Alqaraleh M, Al-Batah MS. Diabetes Prediction and Management Using Machine Learning Approaches. Data Metadata [Internet]. 2025; Available from: https://doi.org/10.56294/dm2025545 DOI: https://doi.org/10.56294/dm2025545
71. Alqaraleh M, Al-Batah MS, Alzboon MS, Alzboon F, Alzboon L, Alamoush MN. Echoes in the Genome: Smoking’s Epigenetic Fingerprints and Bidirectional Neurobiological Pathways in Addiction and Disease. Semin Med Writ Educ [Internet]. 2025; Available from: https://doi.org/10.56294/mw2024.585 DOI: https://doi.org/10.56294/mw2024.585
72. Al-Batah MS. Ranked features selection with MSBRG algorithm and rules classifiers for cervical cancer. Int J Online Biomed Eng. 2019;15(12):4. DOI: https://doi.org/10.3991/ijoe.v15i12.10803
73. Al-Batah MS. Integrating the principal component analysis with partial decision tree in microarray gene data. IJCSNS Int J Comput Sci Netw Secur. 2019;19(3):24-29.
74. SalemAlzboon, Mowafaq and Arif, Suki and Mahmuddin, M and Dakkak O. Peer to Peer Resource Discovery Mechanisms in Grid Computing : A Critical Review. In: The 4th International Conference on Internet Applications, Protocols and Services (NETAPPS2015). 2015. p. 48–54.
75. Al-Batah MS, Al-Eiadeh MR. An improved binary crow-JAYA optimisation system with various evolution operators, such as mutation for finding the max clique in the dense graph. Int J Comput Sci Math. 2024;19(4):327-38. DOI: https://doi.org/10.1504/IJCSM.2024.139088
76. Al-Batah MS, Al-Eiadeh MR. An improved discreet Jaya optimisation algorithm with mutation operator and opposition-based learning to solve the 0-1 knapsack problem. Int J Math Oper Res. 2023;26(2):143-69. DOI: https://doi.org/10.1504/IJMOR.2023.134491
77. Alqaraleh M, Al-Batah MS, Alzboon MS, Alzboon F, Alzboon L, Alamoush MN. From Puffs to Predictions: Leveraging AI, Wearables, and Biomolecular Signatures to Decode Smoking’s Multidimensional Impact on Cardiovascular Health. Semin Med Writ Educ [Internet]. 2025; Available from: https://doi.org/10.56294/mw2024.670 DOI: https://doi.org/10.56294/mw2024.670
78. Al-Batah MS. Testing the probability of heart disease using classification and regression tree model. Annu Res Rev Biol. 2014;4(11):1713-25. DOI: https://doi.org/10.9734/ARRB/2014/7786
79. Alzboon MS, Arif AS, Mahmuddin M. Towards self-resource discovery and selection models in grid computing. ARPN J Eng Appl Sci. 2016;11(10):6269–74.
80. Kapoor S, Sharma A, Verma A, Dhull V, Goyal C. A Comparative Study on Deep Learning and Machine Learning Models for Human Action Recognition in Aerial Videos. Int Arab J Inf Technol [Internet]. 2023;20(4). DOI: https://doi.org/10.34028/iajit/20/4/2
81. Al-Batah MS. Modified recursive least squares algorithm to train the hybrid multilayered perceptron (HMLP) network. Appl Soft Comput. 2010;10(1):236-44. DOI: https://doi.org/10.1016/j.asoc.2009.06.018
82. Alzboon MS, Sintok UUM, Sintok UUM, Arif S. Towards Self-Organizing Infrastructure : A New Architecture for Autonomic Green Cloud Data Centers. ARPN J Eng Appl Sci. 2015;1–7.
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