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Construction and Application of Prediction Model of Diabetes Treatment Effect Based on Improved CART Algorithm

机译:基于改进的购物车算法的糖尿病治疗效果预测模型的构建与应用

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In this research, the rehospitalisation of diabetic patients was taken as the standard to judge the treatment effect- With the help of CART algorithm improved on the basis of Principal Component Analysis, the researcher explored the relationship between multiple data involved in the diabetic treatment program and its treatment effect and built a prediction model of diabetic treatment effect. The study focused on the treatment data from 130 American hospitals. 9000 sets of data were used as the training set, and 1000 sets of data were used as the test set to generate the decision tree, and then the researcher pruned the generated decision tree. The accuracy of the newly established prediction model was greatly improved by 21% and the running time was largely reduced by 5.352s compared with the old model established on the unimproved CART algorithm. The results of the study well verified the feasibility and high efficiency of the new prediction model established on the improved CART algorithm, and provided a new idea for improving the treatment effect and efficiency of diabetes.
机译:在这项研究中,糖尿病患者的再生基础是作为标准的标准,以判断治疗效果 - 在基于主成分分析的基础上提高了Cart算法的帮助,研究人员探讨了糖尿病治疗计划中涉及的多个数据之间的关系其治疗效果并建立了糖尿病治疗效果的预测模型。该研究专注于130名美国医院的治疗数据。使用9000组数据作为训练集,使用1000组数据作为测试集以生成决策树,然后研究人员修剪了生成的决策树。与未提升的购物车算法上建立的旧模型相比,新建立的预测模型的准确性大大提高了21%,运行时间大大降低了5.352秒。研究结果良好验证了在改进的购物车算法上建立的新预测模型的可行性和高效率,并为提高治疗效果和糖尿病效率提供了新的思路。

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