A hybrid intelligent approach to predict discharge diagnosis in pediatric surgical patients
Computer-aided diagnosis is a research area of increasing interest in third-level pediatric hospital care. The effectiveness of surgical treatments improves with accurate and timely information, and machine learning techniques have been employed to assist practitioners in making decisions. In this c...
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MDPI AG
2021
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| author | Avila-George, Himer De-La-Torre, Miguel Castro, Wilson Manuel Dominguez, Danny Turpo Chaparro, Joel Ricardo Sanchez Garces, Jorge Alejandro |
| author_browse | Avila-George, Himer Castro, Wilson Manuel De-La-Torre, Miguel Dominguez, Danny Sanchez Garces, Jorge Alejandro Turpo Chaparro, Joel Ricardo |
| author_facet | Avila-George, Himer De-La-Torre, Miguel Castro, Wilson Manuel Dominguez, Danny Turpo Chaparro, Joel Ricardo Sanchez Garces, Jorge Alejandro |
| author_sort | Avila-George, Himer |
| collection | Producción científica |
| description | Computer-aided diagnosis is a research area of increasing interest in third-level pediatric hospital care. The effectiveness of surgical treatments improves with accurate and timely information, and machine learning techniques have been employed to assist practitioners in making decisions. In this context, the prediction of the discharge diagnosis of new incoming patients could make a difference for successful treatments and optimal resource use. In this paper, a computer-aided diagnosis system is proposed to provide statistical information on the discharge diagnosis of a new incoming patient, based on the historical records from previously treated patients. The proposed system was trained and tested using a dataset of 1196 records; the dataset was coded according to the International Classification of Diseases, version 10 (ICD10). Among the processing steps, relevant features for classification were selected using the sequential forward selection wrapper, and outliers were removed using the density-based spatial clustering of applications with noise. Ensembles of decision trees were trained with different strategies, and the highest classification accuracy was obtained with the extreme Gradient boosting algorithm. A 10-fold cross-validation strategy was employed for system evaluation, and performance comparison was performed in terms of accuracy and F-measure. Experimental results showed an average accuracy of 84.62%, and the resulting decision tree learned from the experience in samples allowed it to visualize suitable treatments related to the historical record of patients. According to computer simulations, the proposed classification approach using XGBoost provided higher classification performance than other ensemble approaches; the resulting decision tree can be employed to inform possible paths and risks according to previous experience learned by the system. Finally, the adaptive system may learn from new cases to increase decisions’ accuracy through incremental learning. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/3185 |
| institution | UPeU |
| language | English |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI AG |
| publisherStr | MDPI AG |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/31852026-10-30T17:10:33ZA hybrid intelligent approach to predict discharge diagnosis in pediatric surgical patientsAvila-George, HimerDe-La-Torre, MiguelCastro, Wilson ManuelDominguez, DannyTurpo Chaparro, Joel RicardoSanchez Garces, Jorge Alejandrocomputer-aided diagnosispediatricssupport vector machinesdecision treesCARTDBSCANComputer-aided diagnosis is a research area of increasing interest in third-level pediatric hospital care. The effectiveness of surgical treatments improves with accurate and timely information, and machine learning techniques have been employed to assist practitioners in making decisions. In this context, the prediction of the discharge diagnosis of new incoming patients could make a difference for successful treatments and optimal resource use. In this paper, a computer-aided diagnosis system is proposed to provide statistical information on the discharge diagnosis of a new incoming patient, based on the historical records from previously treated patients. The proposed system was trained and tested using a dataset of 1196 records; the dataset was coded according to the International Classification of Diseases, version 10 (ICD10). Among the processing steps, relevant features for classification were selected using the sequential forward selection wrapper, and outliers were removed using the density-based spatial clustering of applications with noise. Ensembles of decision trees were trained with different strategies, and the highest classification accuracy was obtained with the extreme Gradient boosting algorithm. A 10-fold cross-validation strategy was employed for system evaluation, and performance comparison was performed in terms of accuracy and F-measure. Experimental results showed an average accuracy of 84.62%, and the resulting decision tree learned from the experience in samples allowed it to visualize suitable treatments related to the historical record of patients. According to computer simulations, the proposed classification approach using XGBoost provided higher classification performance than other ensemble approaches; the resulting decision tree can be employed to inform possible paths and risks according to previous experience learned by the system. Finally, the adaptive system may learn from new cases to increase decisions’ accuracy through incremental learning.Article10.3390/app110835292-s2.0-85104701342https://hdl.handle.net/20.500.15037/3185enApplied SciencesApplied Sciences (Switzerland)2076-3417MDPI AG2021-04-152026-06-27T02:36:21Z2026-06-27T02:36:21Z |
| spellingShingle | computer-aided diagnosis pediatrics support vector machines decision trees CART DBSCAN Avila-George, Himer De-La-Torre, Miguel Castro, Wilson Manuel Dominguez, Danny Turpo Chaparro, Joel Ricardo Sanchez Garces, Jorge Alejandro A hybrid intelligent approach to predict discharge diagnosis in pediatric surgical patients |
| title | A hybrid intelligent approach to predict discharge diagnosis in pediatric surgical patients |
| title_full | A hybrid intelligent approach to predict discharge diagnosis in pediatric surgical patients |
| title_fullStr | A hybrid intelligent approach to predict discharge diagnosis in pediatric surgical patients |
| title_full_unstemmed | A hybrid intelligent approach to predict discharge diagnosis in pediatric surgical patients |
| title_short | A hybrid intelligent approach to predict discharge diagnosis in pediatric surgical patients |
| title_sort | hybrid intelligent approach to predict discharge diagnosis in pediatric surgical patients |
| topic | computer-aided diagnosis pediatrics support vector machines decision trees CART DBSCAN |
| topic_facet | computer-aided diagnosis pediatrics support vector machines decision trees CART DBSCAN |
| url | https://hdl.handle.net/20.500.15037/3185 |
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