基于机器学习算法的心脏病预测诊断模型研究

Research on Predictive Diagnosis Model of Heart Disease Based on Machine Learning Algorithm

以国外某医疗检测机构提供的预测心脏病的开源数据集进行分析和研究。分析引起心脏病的相关因素与患者之间的关系,并构建决策树(DT)和K近邻算法(KNN)两种机器学习算法模型,对心脏病进行分类和预测类别。以准确率(Accuracy)、精确度(Precision)、召回率(Recall)、F1_得分(F1_score)作为模型评价指标,比较和分析了两种机器学习算法模型在分类和预测方面的性能,从而得出最优的模型。研究得出机器学习算法模型为心脏病预测和诊断提供有效的科学依据。

An open source dataset for predicting heart disease provided by a foreign medical testing facility is used for analysis and research.This paper analyzes the relationship between factors associated with causing heart disease and patients,and constructs two machine learning algorithm models of Decision tree(DT)and k-Nearest Neighbor algorithm(KNN),classifies and predicts categories of heart disease.The Accuracy,Precision,Recall,F1_score are used as model evaluation metrics to compare and analyze the performance of the two machine learning algorithm models in classification and prediction aspects,so as to arrive at the optimal model.The research shows that machine learning algorithm models could provide an effective scientific basis for heart disease prediction and diagnosis.