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首页> 外文会议>International Conference on Advanced Computational Intelligence >A Risk Prediction Model for Type 2 Diabetes Based on Weighted Feature Selection of Random Forest and XGBoost Ensemble Classifier
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A Risk Prediction Model for Type 2 Diabetes Based on Weighted Feature Selection of Random Forest and XGBoost Ensemble Classifier

机译：基于随机森林和XGBoost集成分类器加权特征选择的2型糖尿病风险预测模型

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摘要

Type 2 diabetes mellitus is a severe chronic disease threatening human health and has a high incidence worldwide. People need to use effective prediction model to diagnose and prevent diabetes in time. At present, data mining technology has become an increasingly important technology with classification capability in the field of medical diagnosis. This paper proposes a risk prediction model for type 2 diabetes based on ensemble learning method. In the proposed model, the weighted feature selection algorithm based on random forest (RF-WFS) is used for optimal feature selection, and extreme gradient boosting (XGBoost) classifier. The effectiveness of the method was validated by comparing the various performance metrics and the results of different contrast experiments. Additionally, we get a better prediction accuracy using the method than using the other classification algorithms (C4.5, Naive Bayes, AdaBoost, Random Forest). The validation results at UCI Pima Indian diabetes dataset shows that the model has better accuracy and classification performance than other research results mentioned in the literature. As a result, it has been proven that the model would be effective for the diagnosis of diabetes at the initial stage.
机译：2型糖尿病是一种严重的威胁人类健康的慢性疾病，在世界范围内发病率很高。人们需要使用有效的预测模型来及时诊断和预防糖尿病。目前，数据挖掘技术已经成为医学诊断领域中具有分类能力的越来越重要的技术。本文提出了一种基于整体学习方法的2型糖尿病风险预测模型。在该模型中，基于随机森林的加权特征选择算法（RF-WFS）用于最优特征选择和极端梯度增强（XGBoost）分类器。通过比较各种性能指标和不同对比实验的结果，验证了该方法的有效性。此外，与使用其他分类算法（C4.5，朴素贝叶斯，AdaBoost，随机森林）相比，使用该方法可获得更好的预测准确性。 UCI Pima印度糖尿病数据集的验证结果表明，该模型比文献中提到的其他研究结果具有更好的准确性和分类性能。结果，已经证明该模型在初始阶段对糖尿病的诊断将是有效的。

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