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On the Use of Machine Learning Techniques and Non-Invasive Indicators for Classifying and Predicting Cardiac Disorders

Authors :
Raydonal Ospina
Adenice G. O. Ferreira
Hélio M. de Oliveira
Víctor Leiva
Cecilia Castro
Source :
Biomedicines, Vol 11, Iss 10, p 2604 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

This research aims to enhance the classification and prediction of ischemic heart diseases using machine learning techniques, with a focus on resource efficiency and clinical applicability. Specifically, we introduce novel non-invasive indicators known as Campello de Souza features, which require only a tensiometer and a clock for data collection. These features were evaluated using a comprehensive dataset of heart disease cases from a machine learning data repository. Our findings highlight the ability of machine learning algorithms to not only streamline diagnostic procedures but also reduce diagnostic errors and the dependency on extensive clinical testing. Three key features—mean arterial pressure, pulsatile blood pressure index, and resistance-compliance indicator—were found to significantly improve the accuracy of machine learning algorithms in binary heart disease classification. Logistic regression achieved the highest average accuracy among the examined classifiers when utilizing these features. While such novel indicators contribute substantially to the classification process, they should be integrated into a broader diagnostic framework that includes comprehensive patient evaluations and medical expertise. Therefore, the present study offers valuable insights for leveraging data science techniques in the diagnosis and management of cardiovascular diseases.

Details

Language :
English
ISSN :
22279059
Volume :
11
Issue :
10
Database :
Directory of Open Access Journals
Journal :
Biomedicines
Publication Type :
Academic Journal
Accession number :
edsdoj.162ca0781de84e30a8f926d8eccfda81
Document Type :
article
Full Text :
https://doi.org/10.3390/biomedicines11102604