Cargando…

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

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Ali, Sikandar, Hussain, Ali, Aich, Satyabrata, Park, Moo Suk, Chung, Man Pyo, Jeong, Sung Hwan, Song, Jin Woo, Lee, Jae Ha, Kim, Hee Cheol
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8541448/
https://www.ncbi.nlm.nih.gov/pubmed/34685461
http://dx.doi.org/10.3390/life11101092
_version_ 1784589232587669504
author Ali, Sikandar
Hussain, Ali
Aich, Satyabrata
Park, Moo Suk
Chung, Man Pyo
Jeong, Sung Hwan
Song, Jin Woo
Lee, Jae Ha
Kim, Hee Cheol
author_facet Ali, Sikandar
Hussain, Ali
Aich, Satyabrata
Park, Moo Suk
Chung, Man Pyo
Jeong, Sung Hwan
Song, Jin Woo
Lee, Jae Ha
Kim, Hee Cheol
author_sort Ali, Sikandar
collection PubMed
description 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.
format Online
Article
Text
id pubmed-8541448
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-85414482021-10-24 A Soft Voting Ensemble-Based Model for the Early Prediction of Idiopathic Pulmonary Fibrosis (IPF) Disease Severity in Lungs Disease Patients Ali, Sikandar Hussain, Ali Aich, Satyabrata Park, Moo Suk Chung, Man Pyo Jeong, Sung Hwan Song, Jin Woo Lee, Jae Ha Kim, Hee Cheol Life (Basel) Article 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. MDPI 2021-10-15 /pmc/articles/PMC8541448/ /pubmed/34685461 http://dx.doi.org/10.3390/life11101092 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ali, Sikandar
Hussain, Ali
Aich, Satyabrata
Park, Moo Suk
Chung, Man Pyo
Jeong, Sung Hwan
Song, Jin Woo
Lee, Jae Ha
Kim, Hee Cheol
A Soft Voting Ensemble-Based Model for the Early Prediction of Idiopathic Pulmonary Fibrosis (IPF) Disease Severity in Lungs Disease Patients
title A Soft Voting Ensemble-Based Model for the Early Prediction of Idiopathic Pulmonary Fibrosis (IPF) Disease Severity in Lungs Disease Patients
title_full A Soft Voting Ensemble-Based Model for the Early Prediction of Idiopathic Pulmonary Fibrosis (IPF) Disease Severity in Lungs Disease Patients
title_fullStr A Soft Voting Ensemble-Based Model for the Early Prediction of Idiopathic Pulmonary Fibrosis (IPF) Disease Severity in Lungs Disease Patients
title_full_unstemmed A Soft Voting Ensemble-Based Model for the Early Prediction of Idiopathic Pulmonary Fibrosis (IPF) Disease Severity in Lungs Disease Patients
title_short A Soft Voting Ensemble-Based Model for the Early Prediction of Idiopathic Pulmonary Fibrosis (IPF) Disease Severity in Lungs Disease Patients
title_sort soft voting ensemble-based model for the early prediction of idiopathic pulmonary fibrosis (ipf) disease severity in lungs disease patients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8541448/
https://www.ncbi.nlm.nih.gov/pubmed/34685461
http://dx.doi.org/10.3390/life11101092
work_keys_str_mv AT alisikandar asoftvotingensemblebasedmodelfortheearlypredictionofidiopathicpulmonaryfibrosisipfdiseaseseverityinlungsdiseasepatients
AT hussainali asoftvotingensemblebasedmodelfortheearlypredictionofidiopathicpulmonaryfibrosisipfdiseaseseverityinlungsdiseasepatients
AT aichsatyabrata asoftvotingensemblebasedmodelfortheearlypredictionofidiopathicpulmonaryfibrosisipfdiseaseseverityinlungsdiseasepatients
AT parkmoosuk asoftvotingensemblebasedmodelfortheearlypredictionofidiopathicpulmonaryfibrosisipfdiseaseseverityinlungsdiseasepatients
AT chungmanpyo asoftvotingensemblebasedmodelfortheearlypredictionofidiopathicpulmonaryfibrosisipfdiseaseseverityinlungsdiseasepatients
AT jeongsunghwan asoftvotingensemblebasedmodelfortheearlypredictionofidiopathicpulmonaryfibrosisipfdiseaseseverityinlungsdiseasepatients
AT songjinwoo asoftvotingensemblebasedmodelfortheearlypredictionofidiopathicpulmonaryfibrosisipfdiseaseseverityinlungsdiseasepatients
AT leejaeha asoftvotingensemblebasedmodelfortheearlypredictionofidiopathicpulmonaryfibrosisipfdiseaseseverityinlungsdiseasepatients
AT kimheecheol asoftvotingensemblebasedmodelfortheearlypredictionofidiopathicpulmonaryfibrosisipfdiseaseseverityinlungsdiseasepatients
AT alisikandar softvotingensemblebasedmodelfortheearlypredictionofidiopathicpulmonaryfibrosisipfdiseaseseverityinlungsdiseasepatients
AT hussainali softvotingensemblebasedmodelfortheearlypredictionofidiopathicpulmonaryfibrosisipfdiseaseseverityinlungsdiseasepatients
AT aichsatyabrata softvotingensemblebasedmodelfortheearlypredictionofidiopathicpulmonaryfibrosisipfdiseaseseverityinlungsdiseasepatients
AT parkmoosuk softvotingensemblebasedmodelfortheearlypredictionofidiopathicpulmonaryfibrosisipfdiseaseseverityinlungsdiseasepatients
AT chungmanpyo softvotingensemblebasedmodelfortheearlypredictionofidiopathicpulmonaryfibrosisipfdiseaseseverityinlungsdiseasepatients
AT jeongsunghwan softvotingensemblebasedmodelfortheearlypredictionofidiopathicpulmonaryfibrosisipfdiseaseseverityinlungsdiseasepatients
AT songjinwoo softvotingensemblebasedmodelfortheearlypredictionofidiopathicpulmonaryfibrosisipfdiseaseseverityinlungsdiseasepatients
AT leejaeha softvotingensemblebasedmodelfortheearlypredictionofidiopathicpulmonaryfibrosisipfdiseaseseverityinlungsdiseasepatients
AT kimheecheol softvotingensemblebasedmodelfortheearlypredictionofidiopathicpulmonaryfibrosisipfdiseaseseverityinlungsdiseasepatients