中风影响因素分析及预测模型开发:机器学习方法

Juhua Wu, Qide Zhang, Lei Tao, Xiaoyun Lu
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引用次数: 0

摘要

预测是分析脑卒中风险管理的重要手段。本研究探索脑卒中的关键影响因素,采用经典的多层感知(MLP)和径向基函数(RBF)机器学习(ML)算法建立脑卒中预测模型。两个模型分别使用Bagging和Boosting集成学习算法进行训练。并将预测模型的性能与其他经典机器学习算法进行了比较。结果表明:(1)总胆固醇(TC)等9个因素被选为脑卒中预测的主要因素;(2) MLP模型在准确率、泛化程度和评分间信度方面优于RBF模型;(3)对于高维数据集,集成算法优于单一算法。本研究改进了脑卒中的预测方法,对脑卒中的预防有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Influencing Factors Analysis and Prediction Model Development of Stroke: The Machine Learning Approach
Prediction is an important way to analyse stroke risk management. This study explored the critical influencing factors of stroke, used the classical multilayer perception (MLP) and radial basis function (RBF) machine learning (ML) algorithms to develop the model for stroke prediction. The two models were trained with Bagging and Boosting ensemble learning algorithms. The performances of the prediction models were also compared with other classical ML algorithms. The result showed that (1) total cholesterol (TC) and other nine factors were selected as principal factors for the stroke prediction; (2) the MLP model outperformed RBF model in terms of accuracy, generalization and inter-rater reliability; (3) ensemble algorithm was superior to single algorithms for high-dimension dataset in this study. It may come to the conclusion that this study improved the stroke prediction methods and contributed much to the prevention of stroke.
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