Identification of Functional States of the Cardiovascular System According to Flowmetry Data Using Machine Learning Methods
The aim of this work is to analyze the applicability of machine learning methods to the problems of diagnosing on the cardiovascular system data and develop a novel technique for automating this process to support decision making in cardiology. Here, the results obtained with the help of classifiers based on random decision forests are examined, and a proprietary numerical experiment is performed with the Doppler flowmetry data. Considerable attention is paid to data processing and reduction of the input vector dimension for analysis.
Paper
Full text
Identification of Functional States of the Cardiovascular System According to Flowmetry Data Using Machine Learning Methods
Semantic Scholar · Medicine · 2018
Abstract
The aim of this work is to analyze the applicability of machine learning methods to the problems of diagnosing on the cardiovascular system data and develop a novel technique for automating this process to support decision making in cardiology. Here, the results obtained with the help of classifiers based on random decision forests are examined, and a proprietary numerical experiment is performed with the Doppler flowmetry data. Considerable attention is paid to data processing and reduction of the input vector dimension for analysis.