Authors:
Kunti Robiatul Mahmudah
1
;
Bedy Purnama
1
;
2
;
Fatma Indriani
1
;
3
and
Kenji Satou
4
Affiliations:
1
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
;
2
Telkom School of Computing, TELKOM University, Bandung, Indonesia
;
3
Department of Computer Science, Universitas Lambung Mangkurat, Banjarbaru, Indonesia
;
4
Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan
Keyword(s):
Microarray Data, Gene Expression, COPD, Machine Learning, Class Imbalance.
Abstract:
Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory lung disease that causes breathlessness and leads to serious illness including lung cancer. It is estimated that COPD caused 5% of all deaths globally in 2015, putting COPD as the three leading causes of death worldwide. This study proposes methods that utilize gene expression data from microarrays to predict the presence or absence of COPD. The proposed method assists in determining better treatments to lower the fatality rates. In this study, microarray data of the small airway epithelium cells obtained from 135 samples of 23 smokers with COPD (9 GOLD stage I, 12 GOLD stage II, and 2 GOLD stage III), 59 healthy smokers, and 53 healthy nonsmokers were selected from GEO dataset. Machine learning and regression algorithms performed in this study included Random Forest, Support Vector Machine, Naïve Bayes, Gradient Boosting Machines, Elastic Net Regression, and Multiclass Logistic Regression. After diminishing i
mbalance data effect using SMOTE, classification algorithms were performed using 825 of the selected features. High AUC score was achieved by elastic net regression and multiclass logistic regression with AUC of 89% and 90%, respectively. In the metrics including accuracy, specificity, and sensitivity, both classifiers also outperformed the others.
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