Diagnosing asthma and chronic obstructive pulmonary disease with machine learning.

Abstract:

:This study examines the clinical decision support systems in healthcare, in particular about the prevention, diagnosis and treatment of respiratory diseases, such as Asthma and chronic obstructive pulmonary disease. The empirical pulmonology study of a representative sample (n = 132) attempts to identify the major factors that contribute to the diagnosis of these diseases. Machine learning results show that in chronic obstructive pulmonary disease's case, Random Forest classifier outperforms other techniques with 97.7 per cent precision, while the most prominent attributes for diagnosis are smoking, forced expiratory volume 1, age and forced vital capacity. In asthma's case, the best precision, 80.3 per cent, is achieved again with the Random Forest classifier, while the most prominent attribute is MEF2575.

journal_name

Health Informatics J

authors

Spathis D,Vlamos P

doi

10.1177/1460458217723169

subject

Has Abstract

pub_date

2019-09-01 00:00:00

pages

811-827

issue

3

eissn

1460-4582

issn

1741-2811

journal_volume

25

pub_type

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