Development of machine learning model for diagnostic disease prediction based on laboratory tests

Abstract The use of deep learning and machine learning (ML) in medical science is increasing, particularly in the visual, audio, and language data fields. We aimed to build a new optimized ensemble model by blending a DNN (deep neural network) model with two ML models for disease prediction using la...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Dong Jin Park, Min Woo Park, Homin Lee, Young-Jin Kim, Yeongsic Kim, Young Hoon Park
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/efd084658316472ab26393fda73861a0
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:efd084658316472ab26393fda73861a0
record_format	dspace
spelling	oai:doaj.org-article:efd084658316472ab26393fda73861a02021-12-02T14:37:08ZDevelopment of machine learning model for diagnostic disease prediction based on laboratory tests10.1038/s41598-021-87171-52045-2322https://doaj.org/article/efd084658316472ab26393fda73861a02021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87171-5https://doaj.org/toc/2045-2322Abstract The use of deep learning and machine learning (ML) in medical science is increasing, particularly in the visual, audio, and language data fields. We aimed to build a new optimized ensemble model by blending a DNN (deep neural network) model with two ML models for disease prediction using laboratory test results. 86 attributes (laboratory tests) were selected from datasets based on value counts, clinical importance-related features, and missing values. We collected sample datasets on 5145 cases, including 326,686 laboratory test results. We investigated a total of 39 specific diseases based on the International Classification of Diseases, 10th revision (ICD-10) codes. These datasets were used to construct light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost) ML models and a DNN model using TensorFlow. The optimized ensemble model achieved an F1-score of 81% and prediction accuracy of 92% for the five most common diseases. The deep learning and ML models showed differences in predictive power and disease classification patterns. We used a confusion matrix and analyzed feature importance using the SHAP value method. Our new ML model achieved high efficiency of disease prediction through classification of diseases. This study will be useful in the prediction and diagnosis of diseases.Dong Jin ParkMin Woo ParkHomin LeeYoung-Jin KimYeongsic KimYoung Hoon ParkNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dong Jin Park Min Woo Park Homin Lee Young-Jin Kim Yeongsic Kim Young Hoon Park Development of machine learning model for diagnostic disease prediction based on laboratory tests
description	Abstract The use of deep learning and machine learning (ML) in medical science is increasing, particularly in the visual, audio, and language data fields. We aimed to build a new optimized ensemble model by blending a DNN (deep neural network) model with two ML models for disease prediction using laboratory test results. 86 attributes (laboratory tests) were selected from datasets based on value counts, clinical importance-related features, and missing values. We collected sample datasets on 5145 cases, including 326,686 laboratory test results. We investigated a total of 39 specific diseases based on the International Classification of Diseases, 10th revision (ICD-10) codes. These datasets were used to construct light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost) ML models and a DNN model using TensorFlow. The optimized ensemble model achieved an F1-score of 81% and prediction accuracy of 92% for the five most common diseases. The deep learning and ML models showed differences in predictive power and disease classification patterns. We used a confusion matrix and analyzed feature importance using the SHAP value method. Our new ML model achieved high efficiency of disease prediction through classification of diseases. This study will be useful in the prediction and diagnosis of diseases.
format	article
author	Dong Jin Park Min Woo Park Homin Lee Young-Jin Kim Yeongsic Kim Young Hoon Park
author_facet	Dong Jin Park Min Woo Park Homin Lee Young-Jin Kim Yeongsic Kim Young Hoon Park
author_sort	Dong Jin Park
title	Development of machine learning model for diagnostic disease prediction based on laboratory tests
title_short	Development of machine learning model for diagnostic disease prediction based on laboratory tests
title_full	Development of machine learning model for diagnostic disease prediction based on laboratory tests
title_fullStr	Development of machine learning model for diagnostic disease prediction based on laboratory tests
title_full_unstemmed	Development of machine learning model for diagnostic disease prediction based on laboratory tests
title_sort	development of machine learning model for diagnostic disease prediction based on laboratory tests
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/efd084658316472ab26393fda73861a0
work_keys_str_mv	AT dongjinpark developmentofmachinelearningmodelfordiagnosticdiseasepredictionbasedonlaboratorytests AT minwoopark developmentofmachinelearningmodelfordiagnosticdiseasepredictionbasedonlaboratorytests AT hominlee developmentofmachinelearningmodelfordiagnosticdiseasepredictionbasedonlaboratorytests AT youngjinkim developmentofmachinelearningmodelfordiagnosticdiseasepredictionbasedonlaboratorytests AT yeongsickim developmentofmachinelearningmodelfordiagnosticdiseasepredictionbasedonlaboratorytests AT younghoonpark developmentofmachinelearningmodelfordiagnosticdiseasepredictionbasedonlaboratorytests
_version_	1718391044919263232

Development of machine learning model for diagnostic disease prediction based on laboratory tests

Ejemplares similares