Researching Machine Learning Methods for Preventing Cardiovascular Diseases

Daria Grigorieva, Alina Faskhutdinova, Bulat Garafutdinov, V. Mokshin
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Abstract

In this article, a review of existing methods for developing a model for the prevention of cardiovascular diseases was carried out, their advantages and disadvantages were identified. Mortality and morbidity from heart disease has been leading in recent decades throughout the world. The use of various machine learning algorithms, including deep learning algorithms, significantly improves the accuracy of predicting cardiovascular risks of trained models. Using the data obtained, we created a model with which we can identify a group of people who are more at risk of heart disease.
研究预防心血管疾病的机器学习方法
本文对现有的心血管疾病预防模型的建立方法进行了综述,并对其优缺点进行了分析。近几十年来,全世界心脏病的死亡率和发病率一直处于领先地位。使用各种机器学习算法,包括深度学习算法,可以显著提高训练模型预测心血管风险的准确性。利用获得的数据,我们创建了一个模型,用它我们可以识别出一组更容易患心脏病的人。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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