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Open AccessArticle

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

1
Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40292, USA
2
eVida Research Group, University of Deusto, 48007 Bilbao, Spain
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Information 2020, 11(4), 207; https://doi.org/10.3390/info11040207
Received: 23 March 2020 / Revised: 9 April 2020 / Accepted: 10 April 2020 / Published: 14 April 2020
(This article belongs to the Special Issue Artificial Intelligence and Decision Support Systems)
Heart diseases are highly ranked among the leading causes of mortality in the world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number of medical features are reported for patients in the Electronic Health Records (EHR) that allow physicians to diagnose and monitor heart disease. We collected a dataset from Medica Norte Hospital in Mexico that includes 800 records and 141 indicators such as age, weight, glucose, blood pressure rate, and clinical symptoms. Distribution of the collected records is very unbalanced on the different types of heart disease, where 17% of records have hypertensive heart disease, 16% of records have ischemic heart disease, 7% of records have mixed heart disease, and 8% of records have valvular heart disease. Herein, we propose an ensemble-learning framework of different neural network models, and a method of aggregating random under-sampling. To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection. Experiments were conducted with unidirectional and bidirectional neural network models and results showed that an ensemble classifier with a BiLSTM or BiGRU model with a CNN model had the best classification performance with accuracy and F1-score between 91% and 96% for the different types of heart disease. These results are competitive and promising for heart disease dataset. We showed that ensemble-learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset. Our proposed framework can lead to highly accurate models that are adapted for clinical real data and diagnosis use. View Full-Text
Keywords: heart disease classification; neural network; ensemble-learning model; under-sampling; features selection; deep learning heart disease classification; neural network; ensemble-learning model; under-sampling; features selection; deep learning
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MDPI and ACS Style

Baccouche, A.; Garcia-Zapirain, B.; Castillo Olea, C.; Elmaghraby, A. Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico. Information 2020, 11, 207.

AMA Style

Baccouche A, Garcia-Zapirain B, Castillo Olea C, Elmaghraby A. Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico. Information. 2020; 11(4):207.

Chicago/Turabian Style

Baccouche, Asma; Garcia-Zapirain, Begonya; Castillo Olea, Cristian; Elmaghraby, Adel. 2020. "Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico" Information 11, no. 4: 207.

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