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 Jjournal_title
Health informatics journalauthors
Spathis D,Vlamos Pdoi
10.1177/1460458217723169subject
Has Abstractpub_date
2019-09-01 00:00:00pages
811-827issue
3eissn
1460-4582issn
1741-2811journal_volume
25pub_type
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