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Objective: The purpose of this study is to perform analysis through the lowback pain open data set to predict the incidence of non-specific chronic lowback pain (NSLBP) to obtain a more accurate and convenient sagittal spinopelvicparameter model. Methods: The logistic regression analysis and multilayerperceptron(MLP) algorithm is used to construct a NSLBP prediction model basedon the parameters of the spinopelvic parameters from open data source. Results:Degree of spondylolisthesis(DS), Pelvic radius (PR), Sacral slope (SS), Pelvictilt (PT) are four predictors screened out by regression analysis that havesignificant predictive power for the risk of NSLBP. The overall accuracy of theequation prediction model is 85.8%.The MLP network algorithm determines that DSis the most powerful predictor of NSLBP through more precise modeling. Themodel has good predictive ability of 95.2% of accuracy. Conclusions: MLP modelsplay a more accurate role in the construction of predictive models. Computerscience is playing a greater role in helping precision medicine clinicalresearch.

Spinopelvic Anatomic Parameters Prediction Model of NSLBP based on data mining