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Article

A Hybrid Intelligent Approach to Predict Discharge Diagnosis in Pediatric Surgical Patients

1
Departamento de Ciencias Computacionales e Ingenierías, Universidad de Guadalajara, Jalisco 46600, Mexico
2
Facultad de Ingeniería de Industrias Alimentarias, Universidad Nacional de Frontera, Sullana 20103, Peru
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Departamento Cirugía Pediátrica, Hospital Nacional San Bartolomé, Lima 15001, Peru
4
Escuela de Posgrado, Universidad Peruana Unión, Lima 15, Peru
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Facultad de Ingeniería y Arquitectura, Universidad Peruana Unión, Lima 15, Peru
*
Author to whom correspondence should be addressed.
Academic Editor: Donato Cascio
Appl. Sci. 2021, 11(8), 3529; https://doi.org/10.3390/app11083529
Received: 9 March 2021 / Revised: 7 April 2021 / Accepted: 9 April 2021 / Published: 15 April 2021
(This article belongs to the Section Computing and Artificial Intelligence)
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. View Full-Text
Keywords: computer-aided diagnosis; pediatrics; support vector machines; decision trees; CART; DBSCAN; XGBoost; AdaBoost; gradient boosting; voting ensemble; random bagging computer-aided diagnosis; pediatrics; support vector machines; decision trees; CART; DBSCAN; XGBoost; AdaBoost; gradient boosting; voting ensemble; random bagging
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MDPI and ACS Style

Avila-George, H.; De-la-Torre, M.; Castro, W.; Dominguez, D.; Turpo-Chaparro, J.E.; Sánchez-Garcés, J. A Hybrid Intelligent Approach to Predict Discharge Diagnosis in Pediatric Surgical Patients. Appl. Sci. 2021, 11, 3529. https://doi.org/10.3390/app11083529

AMA Style

Avila-George H, De-la-Torre M, Castro W, Dominguez D, Turpo-Chaparro JE, Sánchez-Garcés J. A Hybrid Intelligent Approach to Predict Discharge Diagnosis in Pediatric Surgical Patients. Applied Sciences. 2021; 11(8):3529. https://doi.org/10.3390/app11083529

Chicago/Turabian Style

Avila-George, Himer, Miguel De-la-Torre, Wilson Castro, Danny Dominguez, Josué E. Turpo-Chaparro, and Jorge Sánchez-Garcés. 2021. "A Hybrid Intelligent Approach to Predict Discharge Diagnosis in Pediatric Surgical Patients" Applied Sciences 11, no. 8: 3529. https://doi.org/10.3390/app11083529

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