Background: Knee Osteoarthritis (KOA) is a deteriorating disease that affects human knee joints leading to impaired quality of life with no curative treatments. Timely detection of KOA will guarantee its good management, prevent cartilage impairment and reduce its rate of progression. To heighten its early detection. Objective: This study developed a machine learning ensemble model that improves early clinical diagnosis of the risk of KOA in Adults. Method: The diagnostic results of three machine learning diagnostic models were combined with two ensemble methods proposed to improve the diagnosis of KOA risks. KOA patient dataset used for the modeling of the diagnostic models was obtained from the Federal Medical Hospital located in Ido-Ekiti, Nigeria. Results and Conclusion: The diagnostic result of the base diagnoses models shows higher accuracy than similar recently reviewed research in the literature. Diagnoses results of the two ensemble models confirm their abilities to improve the results of the base models. From the comparison of the diagnoses of the ensemble methods, the Multi Response Linear Regression model leads with 97.77% followed by the Majority Voting model with 96.54% diagnostic accuracy. The Statistical tests employed in this study, validated the ranking of the results recorded by each of the diagnostic models.
| Published in | International Journal of Intelligent Information Systems (Volume 11, Issue 4) |
| DOI | 10.11648/j.ijiis.20221104.11 |
| Page(s) | 51-64 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Osteoarthritis, Clinical-Diagnoses, Ensemble Learning, Computational Intelligence, Improve Diagnoses
| [1] | Ayhan E., Kesmezacar H. & Akgun I. 2014. Intra-articular injections (corticosteroid, hyaluronic acid, platelet-rich plasma) for knee osteoarthritis. World J Orthop. 5 (3), 351–361. |
| [2] | Cross M., Smith E., Hoy D., Nolte S., Ackerman I. & Fransen M. 2014. The Global Burden of Hip and knee Osteoarthritis: Estimates from the Global Burden of Disease 2010 study. Ann Rheum Dis, (73): 1323-1330. |
| [3] | Heidera B., 2011. Knee Osteoarthritis Prevalence, Risk Factors, Pathogenesis, and Features: Part I. Caspian J Intern Med (2): 205-12, 2011. |
| [4] | Palazzo C., Rayaud J., Papelard A., Rayau P. & Poirauden D. S. 2014. The Burden of Musculoskeletal Conditions. Plos ONE. 9 (3: e90633. |
| [5] | Ogunlade, S. O., Alonge, T. O., Omololu, A. B. & Adekolujo, O. S. 2005. Clinical spectrum of large joint osteoarthritis in Ibadan, Nigeria. European J. Science Res. 11: 116-122. |
| [6] | Palazzo C., Nguyen C., M. Lefevre-Colau M., Rannou F. & Poiraudeau S. 2016. Risk Factors and Burden of Osteoarthritis. Annals of Physical and Rehabilitation Medicine. 59 (3): 134-138, DOI: 10.1016/j.rehab.2016.01. 006. |
| [7] | Christensen R., Bartels E. M. & Bliddal A. 2007. Effect of Weight Reduction in Obese Patients Diagnosed with Knee Osteoarthritis: A Systematic Review and Meta-Analysis. Ann Rheum Dis, (66): 433-439. |
| [8] | Zheng H. & Chen C., 2015. Body Mass Index and Risk of Knee Osteoarthritis: Systematic Review and Meta-Analysis of Prospective Studies. BMJ Open.; 5 (12): e007568. DOI: 10.1136/BMJ open-2014-007568. |
| [9] | Murphy L., Schwartz T. A., Helmick C. G, Renner J. B., Tudor G. & Koch G., Dragomir A., Kalsbeek W. D., Luta G. & Jordan J. M. 2008. The lifetime risk of symptomatic knee Osteoarthritis. Arthritis and rheumatism. 59 (9): 1207–1213. https://doi.org/10.1002/art.24021 |
| [10] | Dulay GS, Cooper C, Dennison EM. Knee pain, knee injury, knee osteoarthritis and work. Best Pract Res Clin Rheumatol. 2015; 29 (3): 454–461. |
| [11] | Web1. https://www.mayoclinic.org/diseases-conditions/ Osteoarthritis /symptoms-causes/syc- 20351925 (Accessed 17th October, 2019). |
| [12] | Sharma V., Anuvat K., John L., Davis M. 2017. Scientific American Pain Management - Arthritis of the knee. Decker: Pain-related disease states. |
| [13] | Esser S., Bailey A., 2011. Effects of exercise and physical activity on knee osteoarthritis. Curr Pain Headache Rep. 15 (6): 423–430. |
| [14] | Shinjini K., Beth G. A., Mustapha B., Erik B. D., Shadpour D., Mohammad S. R., Richard G. S., Kenneth L. U. & Gustavo K. R. 2020. Enabling early detection of Osteoarthritis from presymptomatic cartilage texture maps via transport-based learning. Proceedings of the National Academy of Sciences Oct 2020, 117 (40): 24709-24719; DOI: 10.1073/pnas.1917405117. DOI: 10.1109/ICTCS.2019.8923053. |
| [15] | Tiulpin A., Thevenot J., Rahtu E., Lehenkari P. & Saarakkala. 2018. Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach. Sci Rep 8, 1727. https://doi.org/10.1038/s41598-018-20132-7 |
| [16] | Olasehinde O. O. & Olayemi O. C. 2019. Stacked Ensemble Approach to the Development of Lower Respiratory Tract Infection Diagnoses System International Journal of Computer Science and Network, 8 (5): 421-435, ISSN (Online): 2277-5420. www.IJCSN.org |
| [17] | Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter. S. M.., Blau, H. M., & Thrun, S. 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542: 115-118. |
| [18] | Olasehinde O. O., Williams K. & Olayemi O. C. 2018. A Machine Learning Framework for Improving Classification Accuracy Using Stacked Ensemble, Proceedings of the 14th iSTEAMS Multidisciplinary Conference, AlHikmah University, Ilorin, Kwara State, Nigeria. 117-124. |
| [19] | Verma, A. K. & Pal, S. 2020. Prediction of Skin Disease with Three Different Feature Selection Techniques Using Stacking Ensemble Method. Appl Biochem Biotechnol 191, 637–656. https://doi.org/10.1007/s12010-019-03222-8 |
| [20] | Rajaraman S., Candemir S., Alderson P. O., Xue Z., Kohli M., Abuya J., Thoma G. R. & Antani S. 2018. "A novel stacked generalization of models for improved TB detection in chest radiographs," 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, 2018, pp. 718-721, DOI: 10.1109/EMBC.2018.8512337. |
| [21] | Khalid R., 2019. Improving the prediction accuracy of heart disease with ensemble learning and majority voting rule. Chapter 8 in U-Healthcare Monitoring Systems. pp. 179-196. DOI: https://doi.org/10.1016/B978-0-12-815370-3.00008-6. |
| [22] | Yoo J., Lim M. K., Ihm C., Choi E. S. & Kang M. S. 2017. A Study on the Prediction of Rheumatoid Arthritis using Machine Learning. Int. Journal of Applied Engineering Research 12 (20): 9858–9862, 2017. http://www.ripublication.com. |
| [23] | Sheng B., Huang L., Wang X., Zhuang J., Tang L., Deng C., & Zhang Y. 2019. Identification of Knee Osteoarthritis Based on Bayesian Network: Pilot Study’’ JMIR Med Inform; 7 (3): e13562. URL: http://medinform.jmir.org/2019/3/e13562/ DOI: 10.2196/13562 PMID: 31322132. |
| [24] | Jamshidi A., Pelletier J., Martel-Pelletier J. 2019. Machine-Learning-Based Patient-Specific Prediction. Models for Knee Osteoarthritis. Nat. RevRheumatol 15: 49–60. https://doi.org/10.1038/s41584-018-0130-5. |
| [25] | Kluzek S., and Mattei T. A., 2019. Machine-learning for osteoarthritis research. Osteoarthritis Cartilage, 27 (7): 977-978. |
| [26] | Du Y., Almajalid R., Shan J. & Zhang M. 2018. A Novel Method to Predict Knee Osteoarthritis Progression on MRI Using Machine Learning Methods. IEEE Trans Nanobioscience. 17 (3): 228-236, DOI: 10.1109/TNB.2018.2840082. |
| [27] | Jessica K., 2020. Artificial Intelligence May Predict Osteoarthritis Years before Onset. (Accessed 27th November 2020) https://healthitanalytics.com/news/artificial-intelligence-may-predictsteoarthritis- years-before-onset. |
| [28] | Onan A. (2015) On the Performance of Ensemble Learning for Automated Diagnosis of Breast Cancer. In: Silhavy R., Senkerik R., Oplatkova Z., Prokopova Z., Silhavy P. (eds) Artificial Intelligence Perspectives and Applications. Advances in Intelligent Systems and Computing, vol 347. Springer, Cham. https://doi.org/10.1007/978-3-319-18476-0_13 |
| [29] | Oguntimilehin A., Adetunmbi O., & Osho I. 2019. Towards Achieving Optimal Performance using Stacked Generalization Algorithm: A Case Study of Clinical Diagnosis of Malaria Fever. The International Arab Journal of Information Technology, (16) 6. |
| [30] | Stefanus K. TH, Mohd H. A. H., Abdullah B., Razali Y., Mohammad S. J. 2019. Ensemble deep learning for tuberculosis detection using chest X-Ray and canny edge detected images. IAES International Journal of Artificial Intelligence (IJ-AI) (8) 4: 429-435. ISSN: 2252-8938, DOI: 10.11591/ijai.v8.i4.pp429-435. |
| [31] | Christensen R, Bartels EM, Astrup A, et al, 2007. Effect of weight reduction in obese patients diagnosed with knee osteoarthritis: a systematic review and meta-analysis. Annals of the Rheumatic Diseases. (66): 433-439. |
| [32] | Felson D. T., Lawrence R. C., Dieppe P. A., Hirsch R., Helmick C. G., and Jordan J. M.: Osteoarthritis: new Insights. Part 1: The Disease and its Risk Factors. Ann Intern Med, Vol. 133, 635-646, (2000). |
| [33] | Gandhi R, Dhotar H, Tsvetkov D, Mahomed NN. The relation between body mass index and waist-hip ratio in knee osteoarthritis. Can J Surg. 2010 Jun; 53 (3): 151-4. PMID: 20507785; PMCID: PMC2878991. |
| [34] | Richette P., Poitou C., Garnero P., Vicaut E., Bouillot J. L., and Lacorte J. M.: Benefits of Massive Weight Loss on Symptoms, Systemic Inflammation, and Cartilage Turnover in Obese Patients with knee Osteoarthritis. Ann Rheum Dis. Vol. 70, No. 1, 139-144, (2011), DOI: 10.1136/ard.2010.134015. |
| [35] | Dudoit S., Friday J., & Speed T. P. 2002. Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data". J Am Stat Assoc, 97: 77–87. |
| [36] | Janardhanan P., Heena L. & Sabika F. 2015. Effectiveness of Support Vector Machines in Medical Data Mining. Journal of communications software and systems, 11: 25-30. |
| [37] | Wharton W., Kusnanto H. & Herianto H. 2016. Interpretation of Clinical Data Based on C4.5 Algorithm for the Diagnosis of Coronary Heart Disease. Healthcare informatics research, 22 (3), 186–195. https://doi.org/10.4258/hir.2016.22.3.186 |
| [38] | Taheri S. O. N. A., 2015. Learning the Naive Bayes Classifier with Optimization Model. 23 (4): 787–795. DOI: 10.2478/amcs-2013-0059. |
| [39] | Srimani1 P. K. and Manjula S. K. 2013. Medical Diagnosis Using Ensemble Classifiers - A Novel Machine-Learning Approach, Journal of Advanced Computing 1 (9-27) doi: 10.7726/jac.2013.1002 |
| [40] | Atallah R. & Al-Mousa A. 2019. Heart Disease Detection Using Machine Learning, Majority Voting Ensemble Method. 2nd International Conference on New Trends in Computing Sciences (ICTCS), Amman, Jordan, 1-6. |
| [41] | Jeni L. A, Cohn J. F., De La Torre F. (2013). Facing imbalanced data– recommendations for the use of performance metrics, in 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, IEEE, 2013, pp. 245–251. |
| [42] | Siblini W., Fréry J., He-Guelton L., Oblé F., Wang YQ. (2020) Master Your Metrics with Calibration. In: Berthold M., Feelders A., Krempl G. (eds) Advances in Intelligent Data Analysis XVIII. IDA 2020. Lecture Notes in Computer Science, vol 12080. Springer, Cham. https://doi.org/10.1007/978-3-030-44584-3_36 |
| [43] | Kokkotis C., Moustakidis S., Giakas, G., Tsaopoulos, D. 2020. Identification of Risk Factors and Machine Learning-Based Prediction Models for Knee Osteoarthritis Patients. Appl. Sci. 10, 6797. |
| [44] | Akosa, J., 2017. Predictive Accuracy: A Misleading Performance Measure for Highly Imbalanced Data. Proceeding of A New Era of SAS Global AI and Analytics Events, session 0942-2017. https://support.sas.com/resources/papers/proceedings17/0942-2017.pdf |
| [45] | Jack T. (2020, December 2), Beyond Accuracy: other Classification Metrics you should know in Machine Learning. https://towardsdatascience.com/beyond-accuracy-other-classification-metrics-you-should-know-in-machine-learning-ea671be83bb7 |
APA Style
Olayemi Olufunke Catherine, Olasehinde Olayemi Oladimeji, Alowolodu Olufunso Dayo, Osho Patrick Olarewaju. (2022). Ensemble Learning Improvement of Clinical Diagnoses of Knee Osteoarthritis Risk in Adults. International Journal of Intelligent Information Systems, 11(4), 51-64. https://doi.org/10.11648/j.ijiis.20221104.11
ACS Style
Olayemi Olufunke Catherine; Olasehinde Olayemi Oladimeji; Alowolodu Olufunso Dayo; Osho Patrick Olarewaju. Ensemble Learning Improvement of Clinical Diagnoses of Knee Osteoarthritis Risk in Adults. Int. J. Intell. Inf. Syst. 2022, 11(4), 51-64. doi: 10.11648/j.ijiis.20221104.11
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Download@article{10.11648/j.ijiis.20221104.11,
author = {Olayemi Olufunke Catherine and Olasehinde Olayemi Oladimeji and Alowolodu Olufunso Dayo and Osho Patrick Olarewaju},
title = {Ensemble Learning Improvement of Clinical Diagnoses of Knee Osteoarthritis Risk in Adults},
journal = {International Journal of Intelligent Information Systems},
volume = {11},
number = {4},
pages = {51-64},
doi = {10.11648/j.ijiis.20221104.11},
url = {https://doi.org/10.11648/j.ijiis.20221104.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20221104.11},
abstract = {Background: Knee Osteoarthritis (KOA) is a deteriorating disease that affects human knee joints leading to impaired quality of life with no curative treatments. Timely detection of KOA will guarantee its good management, prevent cartilage impairment and reduce its rate of progression. To heighten its early detection. Objective: This study developed a machine learning ensemble model that improves early clinical diagnosis of the risk of KOA in Adults. Method: The diagnostic results of three machine learning diagnostic models were combined with two ensemble methods proposed to improve the diagnosis of KOA risks. KOA patient dataset used for the modeling of the diagnostic models was obtained from the Federal Medical Hospital located in Ido-Ekiti, Nigeria. Results and Conclusion: The diagnostic result of the base diagnoses models shows higher accuracy than similar recently reviewed research in the literature. Diagnoses results of the two ensemble models confirm their abilities to improve the results of the base models. From the comparison of the diagnoses of the ensemble methods, the Multi Response Linear Regression model leads with 97.77% followed by the Majority Voting model with 96.54% diagnostic accuracy. The Statistical tests employed in this study, validated the ranking of the results recorded by each of the diagnostic models.},
year = {2022}
}
TY - JOUR T1 - Ensemble Learning Improvement of Clinical Diagnoses of Knee Osteoarthritis Risk in Adults AU - Olayemi Olufunke Catherine AU - Olasehinde Olayemi Oladimeji AU - Alowolodu Olufunso Dayo AU - Osho Patrick Olarewaju Y1 - 2022/07/26 PY - 2022 N1 - https://doi.org/10.11648/j.ijiis.20221104.11 DO - 10.11648/j.ijiis.20221104.11 T2 - International Journal of Intelligent Information Systems JF - International Journal of Intelligent Information Systems JO - International Journal of Intelligent Information Systems SP - 51 EP - 64 PB - Science Publishing Group SN - 2328-7683 UR - https://doi.org/10.11648/j.ijiis.20221104.11 AB - Background: Knee Osteoarthritis (KOA) is a deteriorating disease that affects human knee joints leading to impaired quality of life with no curative treatments. Timely detection of KOA will guarantee its good management, prevent cartilage impairment and reduce its rate of progression. To heighten its early detection. Objective: This study developed a machine learning ensemble model that improves early clinical diagnosis of the risk of KOA in Adults. Method: The diagnostic results of three machine learning diagnostic models were combined with two ensemble methods proposed to improve the diagnosis of KOA risks. KOA patient dataset used for the modeling of the diagnostic models was obtained from the Federal Medical Hospital located in Ido-Ekiti, Nigeria. Results and Conclusion: The diagnostic result of the base diagnoses models shows higher accuracy than similar recently reviewed research in the literature. Diagnoses results of the two ensemble models confirm their abilities to improve the results of the base models. From the comparison of the diagnoses of the ensemble methods, the Multi Response Linear Regression model leads with 97.77% followed by the Majority Voting model with 96.54% diagnostic accuracy. The Statistical tests employed in this study, validated the ranking of the results recorded by each of the diagnostic models. VL - 11 IS - 4 ER -
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