Abstract
It was to explore the effect of the CT and X-ray examinations before the hip replacement to predict the collapse of the necrotic femoral head under the classification of medical big data based on the decision tree algorithm of the difference grey wolf optimization (GWO) and provide a more effective examination basis for the treatment of patients with the osteonecrosis of the femoral head (ONFH). From January 2019 to January 2021, a total of 152,000 patients with ONFH and hip replacement in the tertiary hospitals were enrolled in this study. They were randomly divided into two groups, the study sample-X group (X-ray examination results) and based-CT group (CT examination results)—76,000 cases in each group. The actual measurement results of the femoral head form the gold standard to evaluate the effect of the two groups of detection methods. The measurement results of X-ray and CT before hip replacement are highly consistent with the detection results of the physical femoral head specimens, which can effectively predict the collapse of ONFH and carry out accurate staging. It is worthy of clinical promotion.
Highlights
According to clinical treatment experience, the reasonable and accurate staging of ONFH has a significant impact on the determination of surgical scheme, treatment effect, and prognosis [14]
Ding et al [19] used the results of the X-ray examination to determine the treatment plan in the study of individual extracorporeal shock wave therapy for early ONFH, and the results showed that the condition of patients with ONFH improved significantly
E generation of the decision tree: the calculation in this part starts with the root node, and the maximum feature is selected from all the potential information gain at node Q as the node feature. en, the child nodes are constructed according to the value of the feature and recursive based on the above calculation method. e mark of the completion of the decision tree construction is that the information gain of all features is small or no feature can be selected
Summary
According to clinical treatment experience, the reasonable and accurate staging of ONFH has a significant impact on the determination of surgical scheme, treatment effect, and prognosis [14]. E results show that the accuracy (0.722), TPR (0.849), FPR (0.201), and F1-score (0.758) improved It is consistent with the research of Supriya and Deepa [20] on a new method for breast cancer prediction based on the neural network classifier optimized in a big data environment. It is consistent with the research of Gao and Zhao [21] on an improved variable weight GWO algorithm, suggesting that the GWO algorithm has a good application prospect [22]
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