Cargando…

Construction of a Diagnostic Algorithm for Diagnosis of Adult Asthma Using Machine Learning with Random Forest and XGBoost

An evidence-based diagnostic algorithm for adult asthma is necessary for effective treatment and management. We present a diagnostic algorithm that utilizes a random forest (RF) and an optimized eXtreme Gradient Boosting (XGBoost) classifier to diagnose adult asthma as an auxiliary tool. Data were g...

Descripción completa

Detalles Bibliográficos
Autores principales: Tomita, Katsuyuki, Yamasaki, Akira, Katou, Ryohei, Ikeuchi, Tomoyuki, Touge, Hirokazu, Sano, Hiroyuki, Tohda, Yuji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10572917/
https://www.ncbi.nlm.nih.gov/pubmed/37835811
http://dx.doi.org/10.3390/diagnostics13193069
_version_ 1785120344394170368
author Tomita, Katsuyuki
Yamasaki, Akira
Katou, Ryohei
Ikeuchi, Tomoyuki
Touge, Hirokazu
Sano, Hiroyuki
Tohda, Yuji
author_facet Tomita, Katsuyuki
Yamasaki, Akira
Katou, Ryohei
Ikeuchi, Tomoyuki
Touge, Hirokazu
Sano, Hiroyuki
Tohda, Yuji
author_sort Tomita, Katsuyuki
collection PubMed
description An evidence-based diagnostic algorithm for adult asthma is necessary for effective treatment and management. We present a diagnostic algorithm that utilizes a random forest (RF) and an optimized eXtreme Gradient Boosting (XGBoost) classifier to diagnose adult asthma as an auxiliary tool. Data were gathered from the medical records of 566 adult outpatients who visited Kindai University Hospital with complaints of nonspecific respiratory symptoms. Specialists made a thorough diagnosis of asthma based on symptoms, physical indicators, and objective testing, including airway hyperresponsiveness. We used two decision-tree classifiers to identify the diagnostic algorithms: RF and XGBoost. Bayesian optimization was used to optimize the hyperparameters of RF and XGBoost. Accuracy and area under the curve (AUC) were used as evaluation metrics. The XGBoost classifier outperformed the RF classifier with an accuracy of 81% and an AUC of 85%. A combination of symptom–physical signs and lung function tests was successfully used to construct a diagnostic algorithm on importance features for diagnosing adult asthma. These results indicate that the proposed model can be reliably used to construct diagnostic algorithms with selected features from objective tests in different settings.
format Online
Article
Text
id pubmed-10572917
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-105729172023-10-14 Construction of a Diagnostic Algorithm for Diagnosis of Adult Asthma Using Machine Learning with Random Forest and XGBoost Tomita, Katsuyuki Yamasaki, Akira Katou, Ryohei Ikeuchi, Tomoyuki Touge, Hirokazu Sano, Hiroyuki Tohda, Yuji Diagnostics (Basel) Article An evidence-based diagnostic algorithm for adult asthma is necessary for effective treatment and management. We present a diagnostic algorithm that utilizes a random forest (RF) and an optimized eXtreme Gradient Boosting (XGBoost) classifier to diagnose adult asthma as an auxiliary tool. Data were gathered from the medical records of 566 adult outpatients who visited Kindai University Hospital with complaints of nonspecific respiratory symptoms. Specialists made a thorough diagnosis of asthma based on symptoms, physical indicators, and objective testing, including airway hyperresponsiveness. We used two decision-tree classifiers to identify the diagnostic algorithms: RF and XGBoost. Bayesian optimization was used to optimize the hyperparameters of RF and XGBoost. Accuracy and area under the curve (AUC) were used as evaluation metrics. The XGBoost classifier outperformed the RF classifier with an accuracy of 81% and an AUC of 85%. A combination of symptom–physical signs and lung function tests was successfully used to construct a diagnostic algorithm on importance features for diagnosing adult asthma. These results indicate that the proposed model can be reliably used to construct diagnostic algorithms with selected features from objective tests in different settings. MDPI 2023-09-27 /pmc/articles/PMC10572917/ /pubmed/37835811 http://dx.doi.org/10.3390/diagnostics13193069 Text en © 2023 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
Tomita, Katsuyuki
Yamasaki, Akira
Katou, Ryohei
Ikeuchi, Tomoyuki
Touge, Hirokazu
Sano, Hiroyuki
Tohda, Yuji
Construction of a Diagnostic Algorithm for Diagnosis of Adult Asthma Using Machine Learning with Random Forest and XGBoost
title Construction of a Diagnostic Algorithm for Diagnosis of Adult Asthma Using Machine Learning with Random Forest and XGBoost
title_full Construction of a Diagnostic Algorithm for Diagnosis of Adult Asthma Using Machine Learning with Random Forest and XGBoost
title_fullStr Construction of a Diagnostic Algorithm for Diagnosis of Adult Asthma Using Machine Learning with Random Forest and XGBoost
title_full_unstemmed Construction of a Diagnostic Algorithm for Diagnosis of Adult Asthma Using Machine Learning with Random Forest and XGBoost
title_short Construction of a Diagnostic Algorithm for Diagnosis of Adult Asthma Using Machine Learning with Random Forest and XGBoost
title_sort construction of a diagnostic algorithm for diagnosis of adult asthma using machine learning with random forest and xgboost
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10572917/
https://www.ncbi.nlm.nih.gov/pubmed/37835811
http://dx.doi.org/10.3390/diagnostics13193069
work_keys_str_mv AT tomitakatsuyuki constructionofadiagnosticalgorithmfordiagnosisofadultasthmausingmachinelearningwithrandomforestandxgboost
AT yamasakiakira constructionofadiagnosticalgorithmfordiagnosisofadultasthmausingmachinelearningwithrandomforestandxgboost
AT katouryohei constructionofadiagnosticalgorithmfordiagnosisofadultasthmausingmachinelearningwithrandomforestandxgboost
AT ikeuchitomoyuki constructionofadiagnosticalgorithmfordiagnosisofadultasthmausingmachinelearningwithrandomforestandxgboost
AT tougehirokazu constructionofadiagnosticalgorithmfordiagnosisofadultasthmausingmachinelearningwithrandomforestandxgboost
AT sanohiroyuki constructionofadiagnosticalgorithmfordiagnosisofadultasthmausingmachinelearningwithrandomforestandxgboost
AT tohdayuji constructionofadiagnosticalgorithmfordiagnosisofadultasthmausingmachinelearningwithrandomforestandxgboost