Department of Physical Therapy for Women's Health, Faculty of Physiotherapy, Deraya University, EL-Minia, Egypt.
Department of Computer Science, Faculty of Science, Minia University, EL-Minia, Egypt.
Sci Rep. 2024 Aug 12;14(1):18726. doi: 10.1038/s41598-024-68798-6.
This paper presents an analysis of trunk movement in women with postnatal low back pain using machine learning techniques. 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 < 0.01, R2 > 0.95) and classified (accuracy > 0.94) 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 machine learning techniques for analyzing trunk movement in women with postnatal low back pain and can potentially inform the development of more effective treatments.Trial registration: 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.
本文应用机器学习技术分析了产后腰痛女性的躯干运动。研究旨在确定与腰痛相关的最重要特征，并开发预测腰痛的准确模型。机器学习方法在分析与产后腰痛（LBP）相关的生物力学因素方面显示出了潜力。本研究将回归和分类算法应用于从 100 名产后女性中提出的躯干运动数据集，其中 50 名患有腰痛，50 名没有。优化的 Optuna Regressor 在回归性能方面表现最佳，均方误差（MSE）为 0.000273，平均绝对误差（MAE）为 0.0039，R2 评分为 0.9968。在分类方面，基本的 CNN 和随机森林分类器都达到了近乎完美的准确率 1.0，接收器工作特征曲线（ROC）下面积（AUC）为 1.0，精确度为 1.0，召回率为 1.0，F1 得分为 1.0，优于其他模型。关键的预测特征包括疼痛（与屈曲运动范围的相关性为-0.732）、运动范围测量（屈曲和伸展的相关性为 0.662）和平均运动（与屈曲的相关性为 0.957）。特征选择一致地确定了疼痛、屈曲、伸展、侧屈和平均运动在各种方法中具有影响力。虽然受到该初始数据集的限制，并受到通用性的限制，但机器学习提供了定量的见解。模型能够准确地回归（MSE<0.01，R2>0.95）和分类（准确率>0.94），区分出腰痛。纳入更多的人口统计学、临床和患者报告因素可能会增强个体化风险预测和治疗个性化。该高级分析的初步应用支持了机器学习在确定 LBP 风险和改善结果方面的潜在效用。本研究为分析产后腰痛女性的躯干运动提供了有价值的见解，并可能为更有效的治疗方法提供信息。
