基于神经网络的脑卒中风险预测模型研究

Predicting Stroke Risks with Neural Network

【目的】完善脑卒中关键风险因素的识别,提高脑卒中风险预测的精度,为脑卒中的科学诊断、治疗和干预提供更多依据。【方法】采集某三甲医院住院部近6000条数据,经过数据处理和逻辑回归建模拟合,筛选出12个重要风险因素并构建脑卒中风险预测多层感知机神经网络模型;利用Python语言构建模型并进行效果检验。【结果】识别出总胆固醇、低密度脂蛋白等是影响脑卒中发病的6个最重要风险因素,当调节隐层神经元个数为7时风险预测模型的预测准确率为97.10%。【局限】有待纳入更多的风险因素和选用多种机器学习模型进行对比分析。【结论】本文方法可快速、有效地从数据中挖掘出影响脑卒中发病的关键风险因素,并预测其发病风险,具有较高的应用价值。

[Objective]This paper tries to effectively predict stroke risks,aiming to improve the diagnoses,treatments and interventions of stroke.[Methods]Firstly,we collected about 6000 inpatient medical records from a top hospital.Then,we identified 12 risk factors affecting stroke with logistic regression modeling.Thirdly,we constructed a multi-layer neural network model to predict stroke risks.Finally,we implemented the model with Python to examine its effectiveness.[Results]I.Total cholesterol and low-density lipoprotein etc.are the most important risk factors affecting the onset of stroke.II.When the number of hidden layer neurons was 7,the risk prediction model accuracy reached 97.10%.[Limitations]We need to include more risk factors and use multiple machine learning models for comparative analyses.[Conclusion]The proposed model could effectively predict the stoke risks facing patients.