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A Soft Voting Ensemble-Based Model for the Early Prediction of Idiopathic Pulmonary Fibrosis (IPF) Disease Severity in Lungs Disease Patients

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Abstract
Idiopathic pulmonary fibrosis, which is one of the lung diseases, is quite rare but fatal in nature. The disease is progressive, and detection of severity takes a long time as well as being quite tedious. With the advent of intelligent machine learning techniques, and also the effectiveness of these techniques, it was possible to detect many lung diseases. So, in this paper, we have proposed a model that could be able to detect the severity of IPF at the early stage so that fatal situations can be controlled. For the development of this model, we used the IPF dataset of the Korean interstitial lung disease cohort data. First, we preprocessed the data while applying different preprocessing techniques and selected 26 highly relevant features from a total of 502 features for 2424 subjects. Second, we split the data into 80% training and 20% testing sets and applied oversampling on the training dataset. Third, we trained three state-of-the-art machine learning models and combined the results to develop a new soft voting ensemble-based model for the prediction of severity of IPF disease in patients with this chronic lung disease. Hyperparameter tuning was also performed to get the optimal performance of the model. Fourth, the performance of the proposed model was evaluated by calculating the accuracy, AUC, confusion matrix, precision, recall, and F1-score. Lastly, our proposed soft voting ensemble-based model achieved the accuracy of 0.7100, precision 0.6400, recall 0.7100, and F1-scores 0.6600. This proposed model will help the doctors, IPF patients, and physicians to diagnose the severity of the IPF disease in its early stages and assist them to take proactive measures to overcome this disease by enabling the doctors to take necessary decisions pertaining to the treatment of IPF disease.

Keywords: idiopathic pulmonary fibrosis disease; machine learning; machine learning prediction; soft voting ensemble.
Author(s)
송진우Ali HussainHee Cheol KimJae Ha LeeMan Pyo ChungMoo Suk ParkSatyabrata AichSikandar AliSung Hwan Jeong
Issued Date
2021
Type
Article
Keyword
idiopathic pulmonary fibrosis diseasemachine learningmachine learning predictionsoft voting ensemble
DOI
10.3390/life11101092
URI
https://oak.ulsan.ac.kr/handle/2021.oak/7180
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_ecd573e333864979917b5fa27338acfa&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,A%20Soft%20Voting%20Ensemble-Based%20Model%20for%20the%20Early%20Prediction%20of%20Idiopathic%20Pulmonary%20Fibrosis%20(IPF)%20Disease%20Severity%20in%20Lungs%20Disease%20Patients&offset=0&pcAvailability=true
Publisher
Life(Basel)
Location
스위스
Language
영어
ISSN
2075-1729
Citation Volume
11
Citation Number
10
Citation Start Page
1092
Citation End Page
1092
Appears in Collections:
Medicine > Medicine
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