论文标题
基于数据挖掘的NSLBP的旋转解剖参数预测模型
Spinopelvic Anatomic Parameters Prediction Model of NSLBP based on data mining
论文作者
论文摘要
目的：这项研究的目的是通过下背痛的打开数据集进行分析，以预测非特异性慢性下背痛（NSLBP）的发生率，以获得更准确，更方便的矢状旋转旋转参数模型。方法：logistic回归分析和多层感知器（MLP）算法用于基于来自开放数据源的spinopelvic参数的参数构建NSLBP预测模型。结果：脊柱滑脱的程度（DS），骨盆半径（PR），骨斜率（SS），骨盆倾斜度（PT）是通过回归分析筛选出的四个预测因子，这些预测因素具有显着的NSLBP风险预测能力。方程预测模型的总体准确性为85.8％。MLP网络算法确定DS是通过更精确的建模是NSLBP最强大的预测指标。该模型具有95.2％的准确性的良好预测能力。结论：MLP模型在预测模型的构建中起着更准确的作用。计算机科学在帮助精确医学临床研究中发挥了更大的作用。
Objective: The purpose of this study is to perform analysis through the low back pain open data set to predict the incidence of non-specific chronic low back pain (NSLBP) to obtain a more accurate and convenient sagittal spinopelvic parameter model. Methods: The logistic regression analysis and multilayer perceptron(MLP) algorithm is used to construct a NSLBP prediction model based on the parameters of the spinopelvic parameters from open data source. Results: Degree of spondylolisthesis(DS), Pelvic radius (PR), Sacral slope (SS), Pelvic tilt (PT) are four predictors screened out by regression analysis that have significant predictive power for the risk of NSLBP. The overall accuracy of the equation prediction model is 85.8%.The MLP network algorithm determines that DS is the most powerful predictor of NSLBP through more precise modeling. The model has good predictive ability of 95.2% of accuracy. Conclusions: MLP models play a more accurate role in the construction of predictive models. Computer science is playing a greater role in helping precision medicine clinical research.
