Research Journal of Pharmacy and Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Lower Back Pain Classification Using Parameter Tuning


Affiliations
1 FICO - Solution Integration - Consultant, Bangalore,, India
2 Assistant Professor, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
     

   Subscribe/Renew Journal


Back pain is one of the most popular diseases which cause extreme discomfort for patients. More than 80% of the people’s day to day activities are affected due to lower back pain. The symptom sometimes gets neglected and worsens the situation, which can cause lifelong damage to vital organs. Lower back pain can be classified as normal and abnormal LBP based on the boundary values of various parameters. Extensive research has been carried out in this field and most of the classification techniques serve the purpose by classifying the data with already provided accuracy values. However, this paper provides a novel technique by adding feature parameter tuning which acts as a catalyst in increasing the accuracy and thereby identifying the effective parameters that help in the optimization.

Keywords

Classification, Categorization, Lower Back Pain, Medical, Parameter tuning.
Subscription Login to verify subscription
User
Notifications
Font Size


  • Gasibat, Q., Simbak, N., Aziz, A. A., and Musa, R. M. The Effect of Strength Training Programme in the Enhancement of Trunk and Hip Muscles Activations among Healthy Females Subject. Research Journal of Pharmacy and Technology, (2017), 10(9), 2845-2850.

  • Staartjes, V. E., Quddusi, A., Klukowska, A. M., and Schröder, M. L. Initial classification of low back and leg pain based on objective functional testing: a pilot study of machine learning applied to diagnostics. European Spine Journal, (2020), 29(7), 1702-1708.

  • Lazennec, J. Y., Brusson, A., and Rousseau, M. A. Lumbar-pelvic-femoral balance on sitting and standing lateral radiographs. Orthopaedics and Traumatology: Surgery and Research, (2013), 99(1), S87-S103.

  • Ung, H., Brown, J. E., Johnson, K. A., Younger, J., Hush, J., and Mackey, S. Multivariate classification of structural MRI data detects chronic low back pain. Cerebral cortex, (2014), 24(4), 1037-1044.

  • Papadopoulos, E. C., and Khan, S. N. Piriformis syndrome and low back pain: a new classification and review of the literature. Orthopedic Clinics, (2004), 35(1), 65-71.

  • Rudwaleit, M., Metter, A., Listing, J., Sieper, J., and Braun, J. Inflammatory back pain in ankylosing spondylitis: a reassessment of the clinical history for application as classification and diagnostic criteria. Arthritis and Rheumatism: Official Journal of the American College of Rheumatology, (2006), 54(2), 569-578.

  • Kajbafvala, M., Rahmani, N., Bandpei, M. A. M., and Salavati, M. Eligibility of the movement-based classification systems in the diagnosis of patients with low back pain: A Systematic Review. Journal of Bodywork and Movement Therapies (2020).

  • Sandag, G. A., Tedry, N. E., and Lolong, S. Classification of lower back pain using K-Nearest Neighbor algorithm. In 2018 6th International Conference on Cyber and IT Service Management (CITSM) (2018) (pp. 1-5). IEEE.

  • Koh, J., Alomari, R. S., Chaudhary, V., and Dhillon, G. Lumbar spinal stenosis CAD from clinical MRM and MRI based on inter-and intra-context features with a two-level classifier. In Medical Imaging 2011: Computer-Aided Diagnosis (2011), (Vol. 7963, p. 796304). International Society for Optics and Photonics.

  • Kelkar, K. M., and Bakal, J. W. Hyper Parameter Tuning of Random Forest Algorithm for Affective Learning System. In 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT) (2020), (pp. 1192-1195). IEEE.

  • Polyak, B. T., and Khlebnikov, M. V. Principle component analysis: Robust versions. Automation and Remote Control, (2017), 78(3), 490-506.

  • Fritz, J. M., Cleland, J. A., and Childs, J. D. Subgrouping patients with low back pain: evolution of a classification approach to physical therapy. journal of orthopaedic and sports physical therapy, (2007), 37(6), 290-302.

  • Wang, G., Xu, J., and He, B. A novel method for tuning configuration parameters of spark based on machine learning. In 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), (2016), (pp. 586-593). IEEE.

  • Luo, Z., Wang, P., Li, Y., Zhang, W., Tang, W., and Xiang, M. Quantum-inspired evolutionary tuning of SVM parameters. Progress in Natural Science, (2008), 18(4), 475-480.

  • Naresh, K., Prabakaran, N., Kannadasan, R., and Boominathan, P. Diabetic Medical Data Classification using Machine Learning Algorithms. Research Journal of Pharmacy and Technology, (2018), 11(1), 97-100.

  • Tarek, S., Abd Elwahab, R., and Shoman, M. Gene expression based cancer classification. Egyptian Informatics Journal, (2017), 18(3), 151-159.

  • Tongchan, T., Pomsing, C., and Tonglim, T. Harmony search algorithm's parameter tuning for traveling salesman problem. In 2017 International Conference on Robotics and Automation Sciences (ICRAS), (2017), (pp. 199-203). IEEE.

  • Swathy, S., and Sethu, V. G. Acupuncture and lower back pain. Research Journal of Pharmacy and Technology, (2015), 8(8), 991.

  • Rojas-Domínguez, A., Padierna, L. C., Valadez, J. M. C., Puga-Soberanes, H. J., and Fraire, H. J. Optimal hyper-parameter tuning of SVM classifiers with application to medical diagnosis. Ieee Access, (2017), 6, 7164-7176.

  • Srilatha, K., and Ulagamuthalvi, V. A Comparative Study on Tumour Classification. Research Journal of Pharmacy and Technology, (2019), 12(1), 407-411.

  • Abdullah, A. A., Yaakob, A., and Ibrahim, Z. Prediction of spinal abnormalities using machine learning techniques. In 2018 International conference on computational approach in smart systems design and applications (ICASSDA) (2018), (pp. 1-6). IEEE.

  • Liu, C. B., Chamberlain, B. P., Little, D. A., and Cardoso, Â. Generalising random forest parameter optimisation to include stability and cost. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (2017), (pp. 102-113). Springer, Cham.

  • Indraja, B., and Annapurani, K. Classification of medicines using naive bayes classifier. Research Journal of Pharmacy and Technology, (2018), 11(5), 1940-1944.

  • Lévesque, J. C., Gagné, C., and Sabourin, R. Bayesian hyperparameter optimization for ensemble learning. arXiv preprint arXiv:1605.06394, (2016).

  • Affrin, J. H. Effect of Prolonged usage of Laptop Related to Neck-Shoulder and Low Back Pain. Research Journal of Pharmacy and Technology, (2018), 11(3), 1217-1219.

  • Roupa, Z., Vassilopoulos, A., Sotiropoulou, P., Makrinika, E., Noula, E., Faros, E., and Marvaki, C. The problem of lower back pain in nursing staff and its effect on human activity. Health science journal, (2008), 2(4).

  • Gasibat, Q., Mesrati, M. H., Musa, R. M., and Zidan, A. A. Effective Recovery and Control of Chronic Low Back Pain by using Rehabilitation Exercises Therapy. Research Journal of Pharmacy and Technology, (2019), 12(9), 4313-4323.

  • Lucas, Y., Domingues, A., Driouchi, D., and Treuillet, S. Design of experiments for performance evaluation and parameter tuning of a road image processing chain. EURASIP Journal on Advances in Signal Processing, (2006), 1-10.

  • Kwon, S., Lee, S., and Na, O. Tuning parameter selection for the adaptive lasso in the autoregressive model. Journal of the Korean Statistical Society, (2017), 46(2), 285-297.

  • Jeganathan, A., Kanhere, A., and Monisha, R. A comparative study to determine the effectiveness of the mckenzie exercise and williams exercise in mechanical low back pain. Research Journal of Pharmacy and Technology, (2018), 11(6), 2440-2443.

  • Urits, I., Burshtein, A., Sharma, M., Testa, L., Gold, P. A., Orhurhu, V., ... and Kaye, A. D. Low back pain, a comprehensive review: pathophysiology, diagnosis, and treatment. Current pain and headache reports, (2019), 23(3), 1-10.

  • Blazquez, C. A., Ries, J., and Miranda, P. A. Towards a parameter tuning approach for a map-matching algorithm. In 2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES), (2017), (pp. 85-90). IEEE.

  • Meenakshi, K., Safa, M., Karthick, T., and Sivaranjani, N. A novel study of machine learning algorithms for classifying health care data. Research Journal of Pharmacy and Technology, (2017), 10(5), 1429.

  • Chau, D. P., Thonnat, M., Bremond, F., and Corvee, E. Online parameter tuning for object tracking algorithms. Image and Vision Computing, (2014), 32(4), 287-302.

  • Kim, D. J., and Kim, J. H. Effects of Mckenzie Exercise on Back Pain and Physical Fitness. Group, (2019), 53(1), 53-338. 36. Sammy [Online]. Available: https://www.kaggle.com/sammy123/lower-back-pain-symptoms-dataset, (2016).

  • Song, L., Minku, L. L., and Yao, X. The impact of parameter tuning on software effort estimation using learning machines. In Proceedings of the 9th international conference on predictive models in software engineering, (2013) (pp. 1-10).

  • Lenka, S., Mishra, S., and Victor, N. IoT based Neonatal Monitoring in the ICU. Research Journal of Pharmacy and Technology, (2019), 12(6), 2885-2888. 39. Popescu, A., and Lee, H. Neck pain and lower back pain. Medical Clinics, (2020), 104(2), 279-292.

  • Venables, W. N., and Smith, D. M. The R development core team. An Introduction to R, (2003), Version, 1(0).


Abstract Views: 291

PDF Views: 0




  • Lower Back Pain Classification Using Parameter Tuning

Abstract Views: 291  |  PDF Views: 0

Authors

Sushmita Lenka
FICO - Solution Integration - Consultant, Bangalore,, India
Nancy Victor
Assistant Professor, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

Abstract


Back pain is one of the most popular diseases which cause extreme discomfort for patients. More than 80% of the people’s day to day activities are affected due to lower back pain. The symptom sometimes gets neglected and worsens the situation, which can cause lifelong damage to vital organs. Lower back pain can be classified as normal and abnormal LBP based on the boundary values of various parameters. Extensive research has been carried out in this field and most of the classification techniques serve the purpose by classifying the data with already provided accuracy values. However, this paper provides a novel technique by adding feature parameter tuning which acts as a catalyst in increasing the accuracy and thereby identifying the effective parameters that help in the optimization.

Keywords


Classification, Categorization, Lower Back Pain, Medical, Parameter tuning.

References