Open AccessPredicting Cardiovascular Events by Machine Learning
Author(s) -
Caiwei Zhang,
Jianhua Qu,
Weicheng Li,
Lehan Zheng
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1693/1/012093
Subject(s) - logistic regression , classifier (uml) , machine learning , artificial intelligence , disease , computer science , field (mathematics) , heart disease , predictive modelling , medicine , cardiology , mathematics , pure mathematics
In the field of medical treatments, machine learning has become an essential technology to mine known data. People always build models to assist doctors to judge. In this paper, 14 features of heart disease patients in two cities, Cleveland and Switzerland, are analyzed by multiple types of neuronal networks and several classifiers after cleaning the data. The model based on the featured data from heart disease patients is designed to predict whether the patients have heart disease or not. Our experimental results on the prediction of cardiovascular events in two cities show that the logistic regression classifier outperforms other methods.