Application of machine learning methods to the recognition of cardiovascular diseases

Abstract

Application of machine learning methods to the recognition of cardiovascular diseases

Alymova A.A., Zubarev K.M., Ivanova T.L.

Incoming article date: 28.08.2024

This work is devoted to the study of the possibility of determining heart diseases on the basis of 13 categorical and numerical signs. We present a detailed analysis of the dataset, including dividing the data into training and test samples, dividing features into numerical and categorical, applying 4 different classification algorithms, checking the quality of the model using two techniques – delayed sampling and cross-validation. To assess the quality of the model, we pay attention to the value of the recall metric and the error matrix built on the test dataset from the deferred sample or on each test fold when using cross-validation. The results of the study are important both for a deep understanding of the relationship between certain medical indicators and heart disease, and for the development of effective methods for predicting them in the presence of individual symptoms.

Keywords: cardiovascular diseases, classification task, quality metrics, cross-validation, recall, machine learning, random forest