Volume 22 , Issue 3 , PP: 99-118, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Khaled Bedair 1 * , Nadir Omer 2 , Ahmed A. H. Abdellatif 3 , Kottakkaran Sooppy Nisar 4 , Shankar Rao Munjam 5 , Ahmed I. Taloba 6
Doi: https://doi.org/10.54216/IJNS.220307
Predicting dorsalgia involves a multifaceted approach that encompasses the analysis of demographic, lifestyle, and medical data. Machine learning algorithms and advanced data analytics play a pivotal role in forecasting the risk of developing back pain. Early prediction aids in proactive interventions and personalized healthcare strategies, thereby mitigating the burden of dorsalgia on individuals and healthcare systems. The proposed feature selection is the initial feature set’s most educational elements by evolutionary gravitational search-based feature selection (EGSFS). Specifically, the framework is trained and fine-tuned using spinal geometry parameters, enabling precise identification of individuals at risk of developing dorsalgia. This study presents a novel approach for classification tasks using a Genetic Algorithm (GA)-optimized hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. The GA optimizes the model’s architecture and hyperparameters to enhance its performance. The framework is implemented using Python. In the categorization procedure, the Single Neutrosophic sets aid in capturing ambiguity, which is particularly beneficial when handling dorsalgia disorders that may present with confusing symptoms, thus enhancing the accuracy of classifying various dorsalgia conditions. Experimental results demonstrate that this hybrid approach significantly improves classification accuracy, making it a viable option for several practical applications. Experimental results exhibit remarkable improvements in accuracy and predictive power, underscoring the potential of this innovative approach in advancing preventative and personalized healthcare strategies for back pain management. The experiment was built on the lower back pain symptoms dataset. A comparison is made between the experimental results and previous prediction models like Logistic Regression, Decision Tree Classifier, Random Forest, and Support Vector Machine in terms of accuracy, F1-score, precision, and recall. The accuracy of normal and abnormal data is 99%.
Dorsalgia , Evolutionary Gravitational Search-Based Feature Selection , Genetic Algorithm , Convolutional Neural Network , Long Short-Term Memory , Machine learning , Single Neutrosophic Sets.
[1] E. VIZITIU and M. CONSTANTINESCU, “Dorsalgia rehabilitation in static disorders of the spine by therapeutic swimming in young adults,” Balneo and PRM Research Journal, vol. 12, no. 1, pp. 82–86, 2021.
[2] Z. E. O ndes¸, I˙. Saral, and E. C¸ akar, “Chiropractic therapy plus multimodal physical therapy versus multimodal physical therapy alone in the management of individuals with cervicothoracic dorsalgia: A randomized clinical trial,” Journal of Complementary Medicine Research, vol. 13, no. 2, pp. 53–53, 2022.
[3] P. Berche, “The enigma of the 1889 russian flu pandemic: A coronavirus?,” La Presse M´edicale, vol. 51, no. 3, p. 104111, 2022.
[4] T. da Silva, K. Mills, B. T. Brown, N. Pocovi, T. de Campos, C. Maher, and M. J. Hancock, “Recurrence of low back pain is common: a prospective inception cohort study,” Journal of physiotherapy, vol. 65, no. 3, pp. 159–165, 2019.
[5] V. Antony Asir Daniel and J. Jeha, “An optimal modified faster region cnn model for diagnosis of liver diseases from ultrasound images,” IETE Journal of Research, pp. 1–18, 2023.
[6] L. K. Nieminen, L. M. Pyysalo, and M. J. Kankaanp¨a¨a, “Prognostic factors for pain chronicity in low back pain: a systematic review,” Pain reports, vol. 6, no. 1, 2021.
[7] Y. Tu, A. Ortiz, R. L. Gollub, J. Cao, J. Gerber, C. Lang, J. Park, G. Wilson, W. Shen, S.-T. Chan, et al., “Multivariate resting-state functional connectivity predicts responses to real and sham acupuncture treatment in chronic low back pain,” NeuroImage: Clinical, vol. 23, p. 101885, 2019.
[8] E. L. Karran, A. R. Grant, and G. L. Moseley, “Low back pain and the social determinants of health: a systematic review and narrative synthesis,” Pain, vol. 161, no. 11, pp. 2476–2493, 2020.
[9] H. Suzuki, S. Aono, S. Inoue, Y. Imajo, N. Nishida, M. Funaba, H. Harada, A. Mori, M. Matsumoto, F. Higuchi, et al., “Clinically significant changes in pain along the pain intensity numerical rating scale in patients with chronic low back pain,” PloS one, vol. 15, no. 3, p. e0229228, 2020.
[10] A. C. Traeger, H. Lee, M. H¨ubscher, I. W. Skinner, G. L. Moseley, M. K. Nicholas, N. Henschke, K. M. Refshauge, F. M. Blyth, C. J. Main, et al., “Effect of intensive patient education vs placebo patient education on outcomes in patients with acute low back pain: a randomized clinical trial,” JAMA neurology, vol. 76, no. 2, pp. 161–169, 2019.
[11] J. A. Hayden, M. N. Wilson, R. D. Riley, R. Iles, T. Pincus, and R. Ogilvie, “Individual recovery expectations and prognosis of outcomes in non-specific low back pain: prognostic factor review,” Cochrane Database of Systematic Reviews, no. 11, 2019.
[12] S. Bunzli, P. O’Brien, N. Klem, I. Incoll, J. Singh, M. Davaris, P. Choong, and M. Dowsey, “Misconceived expectations: patient reflections on the total knee replacement journey,” Musculoskeletal care, vol. 18, no. 4, pp. 415–424, 2020.
[13] N. Mullen, S. Ashby, R. Haskins, and P. Osmotherly, “The perceptions of individuals with musculoskeletal disorders towards prognosis: An exploratory qualitative study,” Musculoskeletal care, vol. 21, no. 2, pp. 527–536, 2023.
[14] B. Widerstr¨om, E. Rasmussen-Barr, and C. Bostr¨om, “Aspects influencing clinical reasoning and decision-making when matching treatment to patients with low back pain in primary healthcare,” Musculoskeletal Science and Practice, vol. 41, pp. 6–14, 2019.
[15] S. M. Rubinstein, A. De Zoete, M. Van Middelkoop, W. J. Assendelft, M. R. De Boer, and M. W. Van Tulder, “Benefits and harms of spinal manipulative therapy for the treatment of chronic low back pain: systematic review and meta-analysis of randomised controlled trials,” bmj, vol. 364, 2019.
[16] F. Al-Sharqi and A. A.-Q. Abd Ghafur Ahmad, “Mapping on interval complex neutrosophic soft sets,” International Journal of Neutrosophic Science, vol. 19, no. 4, pp. 77–85, 2022.
[17] I. Silambarasan, R. Udhayakumar, F. Smarandache, and S. Broumi, “Some algebraic structures of neutrosophic fuzzy sets,” International Journal of Neutrosophic Science, vol. 19, no. 2, pp. 30–41, 2022.
[18] M. Leyva, P. Del Pozo, and A. Pe˜nafiel, “Neutrosophic dematel in the analysis of the causal factors of youth violence,” International Journal of Neutrosophic Science, vol. 18, no. 3, pp. 199–207, 2022.
[19] N. Omer, A. H. Samak, A. I. Taloba, and R. M. Abd El-Aziz, “A novel optimized probabilistic neural network approach for intrusion detection and categorization,” Alexandria Engineering Journal, vol. 72, pp. 351–361, 2023.
[20] M. Alqarni, A. H. Samak, S. S. Ismail, R. M. Abd El-Aziz, A. I. Taloba, et al., “Utilizing a neutrosophic fuzzy logic system with ann for short-term estimation of solar energy,” International Journal of Neutrosophic Science, vol. 20, no. 4, pp. 240–40, 2023.
[21] R. M. Abd El-Aziz, A. I. Taloba, and F. A. Alghamdi, “Quantum computing optimization technique for iot platform using modified deep residual approach,” Alexandria Engineering Journal, vol. 61, no. 12, pp. 12497–12509, 2022.
[22] Z. N. Tekin, Z. Bilgi, O. Akc¸ay, F. Cansun, and E. Kasapo˘glu, “Elastofibroma dorsi prevalance in patients with primary complaint of dorsalgia,” Current Thoracic Surgery, vol. 7, no. 2.
[23] J. R. Afonso, D. Soares, D. B. Lopes, R. M. Matos, and R. P. Pinto, “Osteoartrose costovertebral: Diagnostico diferencial raro de dorsalgia no paciente jovem. relato de caso,” Revista Brasileira de Ortopedia,vol. 57, no. 02, pp. 345–347, 2021.
[24] I. Blass, T. Sahar, A. Shraibman, D. Ofer, N. Rappoport, and M. Linial, “Unified predictive model for endometriosis: Merging clinical, self-reporting and genetic information,” medRxiv, pp. 2022–03, 2022.
[25] P. Kraipeerapun and C. C. Fung, “Binary classification using ensemble neural networks and interval neutrosophic sets,” Neurocomputing, vol. 72, no. 13-15, pp. 2845–2856, 2009.
[26] Z. Li, L. Zhao, J. Ji, B. Ma, Z. Zhao, M. Wu, W. Zheng, and Z. Zhang, “Temporal grading index of functional network topology predicts pain perception of patients with chronic back pain,” Frontiers in Neurology, vol. 13, p. 899254, 2022.
[27] “Lower back pain symptoms dataset.” https://www.kaggle.com/datasets/sammy123/ lower-back-pain-symptoms-dataset, Sept. 2023. Last accessed 05 Sep. 2023.
[28] V. Papageorgiou, “Brain tumor detection based on features extracted and classified using a lowcomplexity neural network.,” Traitement du signal, vol. 38, no. 3, pp. 547–554, 2021.
