Analysis of data mining for classification of Obstructive Sleep Apnea in chronic obstructive pulmonary disease patients

G. D. Apriliana, T. Siswantining, D. Sarwinda, A. Bustamam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Chronic obstructive pulmonary disease (COPD) is one of diseases that could cause a problem of significant concomitant chronic disease which increases morbidity and mortality. COPD is characterized by airflow resistance in the airways caused by airway abnormalities or anatomical abnormalities of the lungs or a combination of both. One complication that can occur in patients with COPD is lack of oxygen intake at night. This situation will be further aggravated if people with COPD also suffer from Obstructive Sleep Apnea (OSA) sleep disorders. In this study, we used Information Gain feature selection to determine which features that affect the risk of OSA in COPD patients. After the feature selection process was completed, we used the Random Forest method to classify who has a high risk and who has a low risk of developing OSA in COPD patients. The sample in this study consist of 111 COPD patients with 34 features who were hospitalized in X Hospital during March 2018 to May 2018. From an observational result, after we choose 5 %, 10 %, 20 %, 30 %, 40 %, 50 %, 60 %, 70 %, 82 %, and 100 % best features of total features, the best accuracy is obtained by 10 % of best features total features (4 best features) i.e. 85.71 % with sensitivity and specificity are 71.43 % and 92.86 % respectively. The feature with the highest ranking is waist size.

Original languageEnglish
Title of host publicationProceedings of the 5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019
EditorsTerry Mart, Djoko Triyono, Tribidasari Anggraningrum Ivandini
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735420014
DOIs
Publication statusPublished - 1 Jun 2020
Event5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019 - Depok, Indonesia
Duration: 9 Jul 201910 Jul 2019

Publication series

NameAIP Conference Proceedings
Volume2242
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019
CountryIndonesia
CityDepok
Period9/07/1910/07/19

Keywords

  • chronic obstructive pulmonary disease
  • information gain
  • obstructive sleep apnea
  • random forest

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  • Cite this

    Apriliana, G. D., Siswantining, T., Sarwinda, D., & Bustamam, A. (2020). Analysis of data mining for classification of Obstructive Sleep Apnea in chronic obstructive pulmonary disease patients. In T. Mart, D. Triyono, & T. A. Ivandini (Eds.), Proceedings of the 5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019 [030023] (AIP Conference Proceedings; Vol. 2242). American Institute of Physics Inc.. https://doi.org/10.1063/5.0007884