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Method Article
Young Hoon Park
Division of Hematology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea
Corresponding Author
ORCiD: https://orcid.org/0000-0003-4516-7990
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Artificial intelligence is a concept that includes machine learning and deep learning. The deep learning model used in this study corresponds to DNN (deep neural network) by utilizing two or more hidden layers. In this study, MLP (multi-layer perceptron) and machine learning models (XGBoost, LGBM) were used. An MLP consists of at least three layers: an input layer, a hidden layer, and an output layer. In general, tree models or linear models using machine learning are widely used for classification. We analyzed our data by applying deep learning (MLP) to improve the performance, which showed good results. 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. Here, we present a protocol to confirm that the use of deep learning can show good performance in disease classification using hospital numerical structured data (laboratory test).
Keywords
Machine learning, Deep neural network, Disease prediction, Laboratory test
Figure 1
This is a list of supplementary files associated with this preprint. Click to download.
- SupplementaryTable9label.docx
Supplementary Table S9. Modified ICD 10 CODE and disease classification
- SupplementaryTable1label.docx
Supplementary Table S1. 88 parameters and abbreviation in this study.
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CURRENT STATUS: Posted
Version 1
Posted 16 Apr, 2021
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Subject Areas
Computational biology and bioinformatics
An open repository of community-contributed protocols sponsored by Nature Research.
Learn more about Protocol Exchange
Method Article
Young Hoon Park
Division of Hematology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea
Corresponding Author
ORCiD: https://orcid.org/0000-0003-4516-7990
Version History
CURRENT STATUS: Posted
Version 1
Posted 16 Apr, 2021
No community comments so far
Metrics
Comments: 0
HTML Views: ...
Subject Areas
Computational biology and bioinformatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Artificial intelligence is a concept that includes machine learning and deep learning. The deep learning model used in this study corresponds to DNN (deep neural network) by utilizing two or more hidden layers. In this study, MLP (multi-layer perceptron) and machine learning models (XGBoost, LGBM) were used. An MLP consists of at least three layers: an input layer, a hidden layer, and an output layer. In general, tree models or linear models using machine learning are widely used for classification. We analyzed our data by applying deep learning (MLP) to improve the performance, which showed good results. 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. Here, we present a protocol to confirm that the use of deep learning can show good performance in disease classification using hospital numerical structured data (laboratory test).
Figure 1
This is a list of supplementary files associated with this preprint. Click to download.
- SupplementaryTable9label.docx
Supplementary Table S9. Modified ICD 10 CODE and disease classification
- SupplementaryTable1label.docx
Supplementary Table S1. 88 parameters and abbreviation in this study.
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