| With the rapid development of big data,a new method,which supports daily routine through data exploration,emerges in broad sectors of society.The healthcare industry,with an extremely large amount of medical data,serves as an example.Information is extracted from data in this industry with the help of the techniques of statistics,machine learning,artificial intelligence,and so on.So far,these techniques have been assisting the work in healthcare industry from four aspects: gene sequencing,medical imaging,auxiliary diagnosis,and drug discovery.Furthermore,medical staffs cooperate and make progress with machine gradually to provide patients with more accurate and more intelligent medical services.This paper focused on the research of auxiliary diagnosis in the field of smart medical service.The auxiliary diagnosis is a diagnostic system which uses basic patient information and disease information and maps the corresponding data to disease inference and diagnosis through machine learning algorithms.In this paper,an orthopedic auxiliary diagnosis classification prediction model based on XGBoost algorithm is constructed for orthopedic medical data.To verify that the model has a better prediction performance,a comparative test was added to the study.The classification prediction model based on the XGBoost algorithm is veried more suitable for orthopedic data and has a better prediction performance compared with the decision tree algorithm and the random forest algorithm.Since the paper mainly focused on the auxiliary diagnosis in the field of smart medical services,it is possible to convert the auxiliary diagnosis problem into a classification problem and establish an auxiliary diagnosis model.The data in this paper originated from the real data of the hospital.In order to ensure that the data can be applied to the model,the data was preprocessed in the process of data collection and organization.After analyzing the characteristics of the data structure,this paper selected the XGBoost algorithm to process medical data with complex data types to assist doctors in diagnosis.In addition,in order to verify the advantages of the prediction model for the classification of orthopedic diagnosis,which is based on XGBoost algorithm,this paper selects three algorithms,the decision tree algorithm,the random forest algorithm and the XGBoost algorithm,to compare with each other.This comparison shows the XGBooost algorithm is an optimal classification algorithm for orthopedic auxiliary diagnosis.Finally,the paper designed a platform equipped with the XGBoost-based prediction model for the classification of orthopedic diagnosis.With the help of this platform,the auxiliary diagnosis of orthopedic diseases will be further realized,and theoretical research can be transformed into practical applications. |
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