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Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction
Cardiovascular diseases (CVDs) have been regarded as the leading cause of death with 32% of the total deaths around the world. Owing to the large number of symptoms related to age, gender, demographics, and ethnicity, diagnosing CVDs is a challenging and complex task. Furthermore, the lack of experi...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9221641/ https://www.ncbi.nlm.nih.gov/pubmed/35741283 http://dx.doi.org/10.3390/diagnostics12061474 |
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author | Rustam, Furqan Ishaq, Abid Munir, Kashif Almutairi, Mubarak Aslam, Naila Ashraf, Imran |
author_facet | Rustam, Furqan Ishaq, Abid Munir, Kashif Almutairi, Mubarak Aslam, Naila Ashraf, Imran |
author_sort | Rustam, Furqan |
collection | PubMed |
description | Cardiovascular diseases (CVDs) have been regarded as the leading cause of death with 32% of the total deaths around the world. Owing to the large number of symptoms related to age, gender, demographics, and ethnicity, diagnosing CVDs is a challenging and complex task. Furthermore, the lack of experienced staff and medical experts, and the non-availability of appropriate testing equipment put the lives of millions of people at risk, especially in under-developed and developing countries. Electronic health records (EHRs) have been utilized for diagnosing several diseases recently and show the potential for CVDs diagnosis as well. However, the accuracy and efficacy of EHRs-based CVD diagnosis are limited by the lack of an appropriate feature set. Often, the feature set is very small and unable to provide enough features for machine learning models to obtain a good fit. This study solves this problem by proposing the novel use of feature extraction from a convolutional neural network (CNN). An ensemble model is designed where a CNN model is used to enlarge the feature set to train linear models including stochastic gradient descent classifier, logistic regression, and support vector machine that comprise the soft-voting based ensemble model. Extensive experiments are performed to analyze the performance of different ratios of feature sets to the training dataset. Performance analysis is carried out using four different datasets and results are compared with recent approaches used for CVDs. Results show the superior performance of the proposed model with 0.93 accuracy, and 0.92 scores each for precision, recall, and F1 score. Results indicate both the superiority of the proposed approach, as well as the generalization of the ensemble model using multiple datasets. |
format | Online Article Text |
id | pubmed-9221641 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92216412022-06-24 Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction Rustam, Furqan Ishaq, Abid Munir, Kashif Almutairi, Mubarak Aslam, Naila Ashraf, Imran Diagnostics (Basel) Article Cardiovascular diseases (CVDs) have been regarded as the leading cause of death with 32% of the total deaths around the world. Owing to the large number of symptoms related to age, gender, demographics, and ethnicity, diagnosing CVDs is a challenging and complex task. Furthermore, the lack of experienced staff and medical experts, and the non-availability of appropriate testing equipment put the lives of millions of people at risk, especially in under-developed and developing countries. Electronic health records (EHRs) have been utilized for diagnosing several diseases recently and show the potential for CVDs diagnosis as well. However, the accuracy and efficacy of EHRs-based CVD diagnosis are limited by the lack of an appropriate feature set. Often, the feature set is very small and unable to provide enough features for machine learning models to obtain a good fit. This study solves this problem by proposing the novel use of feature extraction from a convolutional neural network (CNN). An ensemble model is designed where a CNN model is used to enlarge the feature set to train linear models including stochastic gradient descent classifier, logistic regression, and support vector machine that comprise the soft-voting based ensemble model. Extensive experiments are performed to analyze the performance of different ratios of feature sets to the training dataset. Performance analysis is carried out using four different datasets and results are compared with recent approaches used for CVDs. Results show the superior performance of the proposed model with 0.93 accuracy, and 0.92 scores each for precision, recall, and F1 score. Results indicate both the superiority of the proposed approach, as well as the generalization of the ensemble model using multiple datasets. MDPI 2022-06-15 /pmc/articles/PMC9221641/ /pubmed/35741283 http://dx.doi.org/10.3390/diagnostics12061474 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Rustam, Furqan Ishaq, Abid Munir, Kashif Almutairi, Mubarak Aslam, Naila Ashraf, Imran Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction |
title | Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction |
title_full | Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction |
title_fullStr | Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction |
title_full_unstemmed | Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction |
title_short | Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction |
title_sort | incorporating cnn features for optimizing performance of ensemble classifier for cardiovascular disease prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9221641/ https://www.ncbi.nlm.nih.gov/pubmed/35741283 http://dx.doi.org/10.3390/diagnostics12061474 |
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