{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T12:19:17Z","timestamp":1792412357245,"version":"4.4.0"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T00:00:00Z","timestamp":1723420800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T00:00:00Z","timestamp":1723420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100012533","name":"Minia University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100012533","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper presents an analysis of trunk movement\u00a0in women with\u00a0postnatal low back pain\u00a0using\u00a0machine learning\u00a0techniques. The study aims to identify the most important features related to low back pain and to develop accurate models for predicting low back pain. Machine learning approaches showed promise for analyzing biomechanical factors related to postnatal low back pain (LBP). This study applied regression and classification algorithms to the trunk movement proposed dataset from 100 postpartum women, 50 with LBP and 50 without. The Optimized optuna Regressor achieved the best regression performance with a mean squared error (MSE) of 0.000273, mean absolute error (MAE) of 0.0039, and R2 score of 0.9968. In classification, the Basic CNN and Random Forest Classifier both attained near-perfect accuracy of 1.0, the area under the receiver operating characteristic curve (AUC) of 1.0, precision of 1.0, recall of 1.0, and F1-score of 1.0, outperforming other models. Key predictive features included pain (correlation of -0.732 with flexion range of motion), range of motion measures (flexion and extension correlation of 0.662), and average movements (correlation of 0.957 with flexion). Feature selection consistently identified pain, flexion, extension, lateral flexion, and average movement as influential across methods. While limited to this initial dataset and constrained by generalizability, machine learning offered quantitative insight. Models accurately regressed (MSE\u2009&lt;\u20090.01, R2\u2009&gt;\u20090.95) and classified (accuracy\u2009&gt;\u20090.94) trunk biomechanics distinguishing LBP. Incorporating additional demographic, clinical, and patient-reported factors may enhance individualized risk prediction and treatment personalization. This preliminary application of advanced analytics supported machine learning's potential utility for both LBP risk determination and outcome improvement. This study provides valuable insights into the use of\u00a0machine learning techniques\u00a0for analyzing trunk movement in women with\u00a0postnatal low back\u00a0pain and can potentially inform the development of more effective treatments.<\/jats:p><jats:p>Trial registration<jats:italic>:<\/jats:italic> The trial was designed as an observational and cross-section study. The study was approved by the Ethical Committee in Deraya University, Faculty of Pharmacy, (No: 10\/2023). According to the ethical standards of the Declaration of Helsinki. This study complies with the principles of human research. Each patient signed a written consent form after being given a thorough description of the trial. The study was conducted at the outpatient clinic from February 2023 till June 30, 2023.<\/jats:p>","DOI":"10.1038\/s41598-024-68798-6","type":"journal-article","created":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T19:02:12Z","timestamp":1723489332000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Utilizing machine learning to analyze trunk movement patterns in women with postpartum low back pain"],"prefix":"10.1038","volume":"14","author":[{"given":"Doaa","family":"A. Abdel Hady","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1785-1058","authenticated-orcid":false,"given":"Tarek","family":"Abd El-Hafeez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,12]]},"reference":[{"issue":"1","key":"68798_CR1","doi-asserted-by":"publisher","first-page":"13","DOI":"10.26689\/aogr.v1i1.4942","volume":"1","author":"Y Takagi","year":"2023","unstructured":"Takagi, Y., Hanahara, K. & Tateoka, Y. Actual sleep conditions from the last trimester of pregnancy to three months postpartum and the associated minor problems. Adv. Obstet. Gynecol. Res. 1(1), 13\u201323 (2023).","journal-title":"Adv. Obstet. Gynecol. Res."},{"issue":"1","key":"68798_CR2","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/s12905-022-02023-2","volume":"23","author":"M Zhang","year":"2023","unstructured":"Zhang, M., Cooley, C., Ziadni, M. S., Mackey, I. & Flood, P. Association between history of childbirth and chronic, functionally significant back pain in later life. BMC Womens Health 23(1), 4 (2023).","journal-title":"BMC Womens Health"},{"key":"68798_CR3","doi-asserted-by":"publisher","unstructured":"M. Kinjo, \u201cBack Pain,\u201d in Handbook of Outpatient Medicine, E. Sydney, E. Weinstein, and L. M. Rucker, Eds., Cham: Springer International Publishing, 2018, pp. 371\u2013383. https:\/\/doi.org\/10.1007\/978-3-319-68379-9_23.","DOI":"10.1007\/978-3-319-68379-9_23"},{"issue":"37","key":"68798_CR4","first-page":"141","volume":"11","author":"AH Ahmed","year":"2023","unstructured":"Ahmed, A. H., Hassan, S. I. & Shamekh Taman, A. H. Effect of Kinesio Tape on postpartum low back pain and functional disability in women after cesarean section. Assiut Sci Nurs J 11(37), 141\u2013152 (2023).","journal-title":"Assiut Sci Nurs J"},{"key":"68798_CR5","unstructured":"L. B. Pain, \u201cPost-Partum Care to Restore the Core and Reduce Pain\u201d."},{"issue":"1","key":"68798_CR6","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.ncl.2022.07.002","volume":"41","author":"D Gibbs","year":"2023","unstructured":"Gibbs, D., McGahan, B. G., Ropper, A. E. & Xu, D. S. Back pain: Differential diagnosis and management. Neurol. Clin. 41(1), 61\u201376 (2023).","journal-title":"Neurol. Clin."},{"issue":"1","key":"68798_CR7","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1186\/s12884-023-05780-6","volume":"23","author":"J Li","year":"2023","unstructured":"Li, J. et al. Risk factors associated with attendance at postpartum blood pressure follow-up visit in discharged patients with hypertensive disorders of pregnancy. BMC Pregnancy Childbirth 23(1), 485 (2023).","journal-title":"BMC Pregnancy Childbirth"},{"key":"68798_CR8","unstructured":"Patil, V. R. and Sharma, R. K. \u201cPost pregnancy-symphysis pubis dysfunction and pain management using modified pelvic belts: A review,\u201d in Obstetrics and Gynaecology Forum, 2024, pp. 957\u2013965. Accessed: 15, Jun 2024. [Online]. Available: https:\/\/obstetricsandgynaecologyforum.com\/index.php\/ogf\/article\/view\/404."},{"issue":"6","key":"68798_CR9","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1007\/s11916-022-01041-x","volume":"26","author":"H Li","year":"2022","unstructured":"Li, H., Wang, Y., Oprea, A. D. & Li, J. Postdural puncture headache\u2014risks and current treatment. Curr. Pain Headache Rep. 26(6), 441\u2013452 (2022).","journal-title":"Curr. Pain Headache Rep."},{"key":"68798_CR10","unstructured":"Kituku, J. M. \u201cEvaluation of the practice and adequacy of current pain management following caesarean delivery in patients at Kenyatta national hospital between March & May 2019 a descriptive cohort study,\u201d PhD Thesis, University of Nairobi, 2020. Accessed 15 Jun. 2024. [Online]. Available: http:\/\/erepository.uonbi.ac.ke\/handle\/11295\/153902."},{"issue":"1","key":"68798_CR11","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1186\/s12884-023-05393-z","volume":"23","author":"D Chunmei","year":"2023","unstructured":"Chunmei, D. et al. Self-efficacy associated with regression from pregnancy-related pelvic girdle pain and low back pain following pregnancy. BMC Pregnancy Childbirth 23(1), 122 (2023).","journal-title":"BMC Pregnancy Childbirth"},{"issue":"4","key":"68798_CR12","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1007\/s40520-019-01236-0","volume":"32","author":"N Krishnamurthi","year":"2020","unstructured":"Krishnamurthi, N., Murphey, C. & Driver-Dunckley, E. A comprehensive Movement and Motion training program improves mobility in Parkinson\u2019s disease. Aging Clin. Exp. Res. 32(4), 633\u2013643. https:\/\/doi.org\/10.1007\/s40520-019-01236-0 (2020).","journal-title":"Aging Clin. Exp. Res."},{"issue":"3","key":"68798_CR13","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.ptsp.2004.05.002","volume":"5","author":"P Blanch","year":"2004","unstructured":"Blanch, P. Conservative management of shoulder pain in swimming. Phys. Ther. Sport 5(3), 109\u2013124 (2004).","journal-title":"Phys. Ther. Sport"},{"issue":"3","key":"68798_CR14","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.jphys.2023.05.017","volume":"69","author":"SL Gluppe","year":"2023","unstructured":"Gluppe, S. L., Engh, A. M. & B\u00f8, K. Curl-up exercises improve abdominal muscle strength without worsening inter-recti distance in women with diastasis recti abdominis postpartum: A randomised controlled trial. J. Physiother. 69(3), 160\u2013167 (2023).","journal-title":"J. Physiother."},{"issue":"1","key":"68798_CR15","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1080\/10485250500473099","volume":"18","author":"G Shieh","year":"2006","unstructured":"Shieh, G., Jan, S. & Randles, R. On power and sample size determinations for the Wilcoxon\u2013Mann\u2013Whitney test. J. Nonparametric Stat. 18(1), 33\u201343 (2006).","journal-title":"J. Nonparametric Stat."},{"key":"68798_CR16","unstructured":"\u201cUniversit\u00e4t D\u00fcsseldorf: G*Power.\u201d Accessed 21 Jul 2023. [Online]. Available: https:\/\/www.psychologie.hhu.de\/arbeitsgruppen\/allgemeine-psychologie-und-arbeitspsychologie\/gpower"},{"issue":"6","key":"68798_CR17","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1016\/j.jpainsymman.2010.08.016","volume":"41","author":"MJ Hjermstad","year":"2011","unstructured":"Hjermstad, M. J. et al. Studies comparing numerical rating scales, verbal rating scales, and visual analogue scales for assessment of pain intensity in adults: A systematic literature review. J. Pain Symptom Manag. 41(6), 1073\u20131093 (2011).","journal-title":"J. Pain Symptom Manag."},{"issue":"4","key":"68798_CR18","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1053\/ajem.2002.33778","volume":"20","author":"EJ Gallagher","year":"2002","unstructured":"Gallagher, E. J., Bijur, P. E., Latimer, C. & Silver, W. Reliability and validity of a visual analog scale for acute abdominal pain in the ED. Am. J. Emerg. Med. 20(4), 287\u2013290 (2002).","journal-title":"Am. J. Emerg. Med."},{"issue":"4","key":"68798_CR19","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1136\/ard.37.4.378","volume":"37","author":"WW Downie","year":"1978","unstructured":"Downie, W. W. et al. Studies with pain rating scales. Ann. Rheum. Dis. 37(4), 378\u2013381 (1978).","journal-title":"Ann. Rheum. Dis."},{"key":"68798_CR20","doi-asserted-by":"publisher","first-page":"e23932","DOI":"10.1002\/ajhb.23932","volume":"35","author":"S Ghosh","year":"2023","unstructured":"Ghosh, S. Human adaptation to cold and warm climatic conditions: A comparison between two geographically diverse Indigenous populations. Am. J. Hum. Biol. 35, e23932 (2023).","journal-title":"Am. J. Hum. Biol."},{"key":"68798_CR21","unstructured":"Norkin, C. C. and White, D. J., Measurement of Joint Motion: a Guide to Goniometry. FA Davis, 2016. Accessed 14 Jun 2024. [Online]. Available: https:\/\/www.google.com\/books?hl=en&lr=&id=TSluDQAAQBAJ&oi=fnd&pg=PR1&dq=3-%09+Norkin,+C.C.%3B+White,+D.J.+Measuring+Joint+Motion:+A+Guide+to+Goniometry,+4th+ed.%3B+F.A.+Davis+Company:+Philadelphia,+PA,+USA,+2016.&ots=2i2XubAgzZ&sig=6dkFw11-NiyloVtjIt_DpyvyuL4."},{"issue":"4","key":"68798_CR22","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1590\/S0102-36162010000400008","volume":"45","author":"C Chertman","year":"2010","unstructured":"Chertman, C. et al. A comparative study of lumbar range of movement in healthy athletes and non-athletes. Rev. Bras. Ortop. Engl. Ed. 45(4), 389\u2013394 (2010).","journal-title":"Rev. Bras. Ortop. Engl. Ed."},{"issue":"6888","key":"68798_CR23","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1136\/bmj.306.6888.1299","volume":"306","author":"R Russell","year":"1993","unstructured":"Russell, R., Groves, P., Taub, N., O\u2019Dowd, J. & Reynolds, F. Assessing long-term backache after childbirth. Br. Med. J. 306(6888), 1299\u20131303 (1993).","journal-title":"Br. Med. J."},{"issue":"3","key":"68798_CR24","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.math.2004.10.004","volume":"10","author":"M Troke","year":"2005","unstructured":"Troke, M., Moore, A. P., Maillardet, F. J. & Cheek, E. A normative database of lumbar spine ranges of motion. Man. Ther. 10(3), 198\u2013206 (2005).","journal-title":"Man. Ther."},{"key":"68798_CR25","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1007\/s00586-010-1487-6","volume":"20","author":"A Gutke","year":"2011","unstructured":"Gutke, A., Lundberg, M., \u00d6stgaard, H. C. & \u00d6berg, B. Impact of postpartum lumbopelvic pain on disability, pain intensity, health-related quality of life, activity level, kinesiophobia, and depressive symptoms. Eur. Spine J. 20, 440\u2013448 (2011).","journal-title":"Eur. Spine J."},{"issue":"5","key":"68798_CR26","doi-asserted-by":"publisher","first-page":"1654","DOI":"10.3390\/s18051654","volume":"18","author":"A Nait Aicha","year":"2018","unstructured":"Nait Aicha, A., Englebienne, G., Van Schooten, K. S., Pijnappels, M. & Kr\u00f6se, B. Deep learning to predict falls in older adults based on daily-life trunk accelerometry. Sensors. 18(5), 1654 (2018).","journal-title":"Sensors."},{"issue":"12","key":"68798_CR27","doi-asserted-by":"publisher","first-page":"3600","DOI":"10.3390\/s20123600","volume":"20","author":"M Abdollahi","year":"2020","unstructured":"Abdollahi, M. et al. Using a motion sensor to categorize nonspecific low back pain patients: A machine learning approach. Sensors 20(12), 3600 (2020).","journal-title":"Sensors"},{"issue":"12","key":"68798_CR28","doi-asserted-by":"publisher","first-page":"2953","DOI":"10.1007\/s11517-020-02258-x","volume":"58","author":"S Rothstock","year":"2020","unstructured":"Rothstock, S., Weiss, H.-R., Krueger, D. & Paul, L. Clinical classification of scoliosis patients using machine learning and markerless 3D surface trunk data. Med. Biol. Eng. Comput. 58(12), 2953\u20132962. https:\/\/doi.org\/10.1007\/s11517-020-02258-x (2020).","journal-title":"Med. Biol. Eng. Comput."},{"issue":"2","key":"68798_CR29","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1109\/TBME.2020.3012783","volume":"68","author":"A Moniri","year":"2020","unstructured":"Moniri, A., Terracina, D., Rodriguez-Manzano, J., Strutton, P. H. & Georgiou, P. Real-time forecasting of sEMG features for trunk muscle fatigue using machine learning. IEEE Trans. Biomed. Eng. 68(2), 718\u2013727 (2020).","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"17","key":"68798_CR30","doi-asserted-by":"publisher","first-page":"6694","DOI":"10.3390\/s22176694","volume":"22","author":"TC Phan","year":"2022","unstructured":"Phan, T. C. et al. Machine learning derived lifting techniques and pain self-efficacy in people with chronic low back pain. Sensors 22(17), 6694 (2022).","journal-title":"Sensors"},{"issue":"13","key":"68798_CR31","doi-asserted-by":"publisher","first-page":"5027","DOI":"10.3390\/s22135027","volume":"22","author":"P Thiry","year":"2022","unstructured":"Thiry, P. et al. Machine learning identifies chronic low back pain patients from an instrumented trunk bending and return test. Sensors 22(13), 5027 (2022).","journal-title":"Sensors"},{"key":"68798_CR32","doi-asserted-by":"publisher","first-page":"41676","DOI":"10.1109\/ACCESS.2024.3377111","volume":"12","author":"AZ Rao","year":"2024","unstructured":"Rao, A. Z. et al. Sensor fusion and machine learning for seated movement detection with trunk orthosis. IEEE Access 12, 41676\u201341687 (2024).","journal-title":"IEEE Access"},{"key":"68798_CR33","unstructured":"tarekhemdan, \u201ctarekhemdan\/Trunk_Movement.\u201d Jul. 05, 2023. Accessed 21 Jul, 2023. [Online]. Available: https:\/\/github.com\/tarekhemdan\/Trunk_Movement."},{"issue":"9","key":"68798_CR34","doi-asserted-by":"publisher","first-page":"1871","DOI":"10.1007\/s11869-023-01380-7","volume":"16","author":"Y Yin","year":"2023","unstructured":"Yin, Y. & Liu, H. Air quality index prediction based on three-stage feature engineering, model matching, and optimized ensemble. Air Qual. Atmosphere Health. 16(9), 1871\u20131890 (2023).","journal-title":"Air Qual. Atmosphere Health."},{"issue":"4","key":"68798_CR35","doi-asserted-by":"publisher","first-page":"2913","DOI":"10.1007\/s11277-022-10079-4","volume":"128","author":"J Naskath","year":"2023","unstructured":"Naskath, J., Sivakamasundari, G. & Begum, A. A. S. A study on different deep learning algorithms used in deep neural nets: MLP SOM and DBN. Wirel. Pers. Commun. 128(4), 2913\u20132936 (2023).","journal-title":"Wirel. Pers. Commun."},{"issue":"3","key":"68798_CR36","doi-asserted-by":"publisher","first-page":"590","DOI":"10.3390\/math11030590","volume":"11","author":"S Zaheer","year":"2023","unstructured":"Zaheer, S. et al. A multi-parameter forecasting for stock time series data using LSTM and deep learning model. Mathematics 11(3), 590 (2023).","journal-title":"Mathematics"},{"key":"68798_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2023.102119","volume":"75","author":"A Dehghani","year":"2023","unstructured":"Dehghani, A. et al. Comparative evaluation of LSTM, CNN, and ConvLSTM for hourly short-term streamflow forecasting using deep learning approaches. Ecol. Inform. 75, 102119 (2023).","journal-title":"Ecol. Inform."},{"issue":"2","key":"68798_CR38","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1016\/j.ijforecast.2022.02.010","volume":"39","author":"D Borup","year":"2023","unstructured":"Borup, D., Christensen, B. J., M\u00fchlbach, N. S. & Nielsen, M. S. Targeting predictors in random forest regression. Int. J. Forecast. 39(2), 841\u2013868 (2023).","journal-title":"Int. J. Forecast."},{"issue":"3","key":"68798_CR39","doi-asserted-by":"publisher","first-page":"2909","DOI":"10.1007\/s00521-022-07724-1","volume":"35","author":"HU Ahmed","year":"2023","unstructured":"Ahmed, H. U., Mostafa, R. R., Mohammed, A., Sihag, P. & Qadir, A. Support vector regression (SVR) and grey wolf optimization (GWO) to predict the compressive strength of GGBFS-based geopolymer concrete. Neural Comput. Appl. 35(3), 2909\u20132926 (2023).","journal-title":"Neural Comput. Appl."},{"key":"68798_CR40","doi-asserted-by":"publisher","unstructured":"P. Sarang, \u201cEnsemble: Bagging and Boosting,\u201d in Thinking Data Science: A Data Science Practitioner\u2019s Guide, P. Sarang, Ed., Cham: Springer International Publishing, 2023, pp. 97\u2013129. https:\/\/doi.org\/10.1007\/978-3-031-02363-7_5.","DOI":"10.1007\/978-3-031-02363-7_5"},{"issue":"1","key":"68798_CR41","doi-asserted-by":"publisher","first-page":"11004","DOI":"10.1038\/s41598-024-60387-x","volume":"14","author":"DA Abdel Hady","year":"2024","unstructured":"Abdel Hady, D. A., Mabrouk, O. M. & Abd, E.-H. Employing machine learning for enhanced abdominal fat prediction in cavitation post-treatment. Sci. Rep. 14(1), 11004 (2024).","journal-title":"Sci. Rep."},{"issue":"1","key":"68798_CR42","doi-asserted-by":"publisher","first-page":"4795","DOI":"10.1038\/s41598-024-54967-0","volume":"14","author":"DA Abdel Hady","year":"2024","unstructured":"Abdel Hady, D. A. & Abd, E.-H. Revolutionizing core muscle analysis in female sexual dysfunction based on machine learning. Sci. Rep. 14(1), 4795. https:\/\/doi.org\/10.1038\/s41598-024-54967-0 (2024).","journal-title":"Sci. Rep."},{"issue":"1","key":"68798_CR43","doi-asserted-by":"publisher","first-page":"17940","DOI":"10.1038\/s41598-023-44964-0","volume":"13","author":"DA Abdel Hady","year":"2023","unstructured":"Abdel Hady, D. A. & Abd, E.-H. Predicting female pelvic tilt and lumbar angle using machine learning in case of urinary incontinence and sexual dysfunction. Sci. Rep. 13(1), 17940. https:\/\/doi.org\/10.1038\/s41598-023-44964-0 (2023).","journal-title":"Sci. Rep."},{"issue":"1","key":"68798_CR44","doi-asserted-by":"publisher","first-page":"8462056","DOI":"10.1155\/2024\/8462056","volume":"2024","author":"EH Eliwa","year":"2024","unstructured":"Eliwa, E. H., El Koshiry, A. M., Abd El-Hafeez, T. & Omar, A. Optimal gasoline price predictions: Leveraging the ANFIS regression model. Int. J. Intell. Syst. 2024(1), 8462056. https:\/\/doi.org\/10.1155\/2024\/8462056 (2024).","journal-title":"Int. J. Intell. Syst."},{"issue":"34","key":"68798_CR45","doi-asserted-by":"publisher","first-page":"2091","DOI":"10.1016\/j.eswa.2007.02.012","volume":"3","author":"S Bibi","year":"2008","unstructured":"Bibi, S., Tsoumakas, G., Stamelos, I. & Vlahavas, I. Regression via Classification applied on software defect estimation. Expert Syst. Appl. 3(34), 2091\u20132101. https:\/\/doi.org\/10.1016\/j.eswa.2007.02.012 (2008).","journal-title":"Expert Syst. Appl."},{"issue":"249","key":"68798_CR46","doi-asserted-by":"publisher","first-page":"123608","DOI":"10.1016\/j.eswa.2024.123608","volume":"1","author":"MY Shams","year":"2024","unstructured":"Shams, M. Y., Abd El-Hafeez, T. & Hassan, E. Acoustic data detection in large-scale emergency vehicle sirens and road noise dataset. Expert Syst. Appl. 1(249), 123608. https:\/\/doi.org\/10.1016\/j.eswa.2024.123608 (2024).","journal-title":"Expert Syst. Appl."},{"key":"68798_CR47","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1961","volume":"10","author":"AME Koshiry","year":"2024","unstructured":"Koshiry, A. M. E., Eliwa, E. H. I., El-Hafeez, T. A. & Khairy, M. Detecting cyberbullying using deep learning techniques: A pre-trained glove and focal loss technique. PeerJ Comput. Sci. 10, e1961. https:\/\/doi.org\/10.7717\/peerj-cs.1961 (2024).","journal-title":"PeerJ Comput. Sci."},{"key":"68798_CR48","unstructured":"S. Raschka, \u201cAn Overview of General Performance Metrics of Binary Classifier Systems,\u201d ArXiv Prepr. ArXiv14105330, 2014."},{"issue":"8","key":"68798_CR49","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1097\/01.TPM.0001007768.04561.6a","volume":"39","author":"P Strube","year":"2024","unstructured":"Strube, P. Are pain specialists failing in treating patients with low back pain?. Top. Pain Manag. 39(8), 6\u20137 (2024).","journal-title":"Top. Pain Manag."},{"key":"68798_CR50","doi-asserted-by":"publisher","first-page":"100287","DOI":"10.1016\/j.metop.2024.100287","volume":"19","author":"S Singh","year":"2024","unstructured":"Singh, S. et al. Deciphering the complex interplay of risk factors in type 2 diabetes mellitus: A comprehensive review. Metab. Open. 19, 100287 (2024).","journal-title":"Metab. Open."},{"issue":"3","key":"68798_CR51","doi-asserted-by":"publisher","first-page":"831","DOI":"10.3390\/s24030831","volume":"24","author":"F Villalba-Meneses","year":"2024","unstructured":"Villalba-Meneses, F. et al. Classification of the pathological range of motion in low back pain using wearable sensors and machine learning. Sensors 24(3), 831 (2024).","journal-title":"Sensors"},{"issue":"1","key":"68798_CR52","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1186\/s13037-024-00403-1","volume":"18","author":"E Alimohammadi","year":"2024","unstructured":"Alimohammadi, E., Fatahi, E., Abdi, A. & Reza, B. S. Assessing the predictive capability of machine learning models in determining clinical outcomes for patients with cervical spondylotic myelopathy treated with laminectomy and posterior spinal fusion. Patient Saf. Surg. 18(1), 21 (2024).","journal-title":"Patient Saf. Surg."},{"issue":"4","key":"68798_CR53","doi-asserted-by":"publisher","first-page":"1337","DOI":"10.3390\/s24041337","volume":"24","author":"TC Phan","year":"2024","unstructured":"Phan, T. C. et al. Regression-based machine learning for predicting lifting movement pattern change in people with low back pain. Sensors 24(4), 1337 (2024).","journal-title":"Sensors"},{"issue":"1","key":"68798_CR54","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1177\/21925682221097650","volume":"14","author":"G Ren","year":"2024","unstructured":"Ren, G. et al. Machine learning predicts recurrent lumbar disc herniation following percutaneous endoscopic lumbar discectomy. Glob. Spine J. 14(1), 146\u2013152. https:\/\/doi.org\/10.1177\/21925682221097650 (2024).","journal-title":"Glob. Spine J."},{"issue":"1","key":"68798_CR55","doi-asserted-by":"publisher","first-page":"e54622","DOI":"10.2196\/54622","volume":"12","author":"E Hurwitz","year":"2024","unstructured":"Hurwitz, E. et al. Harnessing consumer wearable digital biomarkers for individualized recognition of postpartum depression using the all of us research program data set: Cross-sectional study. JMIR MHealth UHealth 12(1), e54622 (2024).","journal-title":"JMIR MHealth UHealth"},{"issue":"4","key":"68798_CR56","doi-asserted-by":"publisher","first-page":"pzad171","DOI":"10.1093\/ptj\/pzad171","volume":"104","author":"S Vesting","year":"2024","unstructured":"Vesting, S., Gutke, A., Fagevik Ols\u00e9n, M., Rembeck, G. & Larsson, M. E. The impact of exercising on pelvic symptom severity, pelvic floor muscle strength, and diastasis recti abdominis after pregnancy: A longitudinal prospective cohort study. Phys. Ther. 104(4), pzad171 (2024).","journal-title":"Phys. Ther."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-68798-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-68798-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-68798-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T23:05:54Z","timestamp":1723763154000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-68798-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,12]]},"references-count":56,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["68798"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-68798-6","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,12]]},"assertion":[{"value":"11 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"18726"}}