Detection of Cardiovascular Diseases Using Predictive Models Based on Deep Learning Techniques: A Hybrid Neutrosophic AHP-TOPSIS Approach for Model Selection
In Ecuador and globally, cardiovascular diseases are the leading cause of mortality, accounting for a worrying 26.49% of deaths in 2019. An approach based on deep learning is applied to improve the capacity for early prediction and reduce its incidence. In this work, three different models were prop...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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University of New Mexico
2024-12-01
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| Series: | Neutrosophic Sets and Systems |
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| Online Access: | https://fs.unm.edu/NSS/17CardiovascularDiseases.pdf |
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| _version_ | 1849228937464381440 |
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| author | Julio Barzola-Monteses Rosangela Caicedo-Quiroz Franklin Parrales-Bravo Cristhian Medina-Suarez Wendy Yanez-Pazmino David Zabala-Blanco Maikel Y. Leyva-Vazquez |
| author_facet | Julio Barzola-Monteses Rosangela Caicedo-Quiroz Franklin Parrales-Bravo Cristhian Medina-Suarez Wendy Yanez-Pazmino David Zabala-Blanco Maikel Y. Leyva-Vazquez |
| author_sort | Julio Barzola-Monteses |
| collection | DOAJ |
| description | In Ecuador and globally, cardiovascular diseases are the leading cause of mortality, accounting for a worrying 26.49% of deaths in 2019. An approach based on deep learning is applied to improve the capacity for early prediction and reduce its incidence. In this work, three different models were proposed and compared: deep neural networks (DNN), convolutional neural networks (CNN), and multilayer perceptron (MLP). Experiments were conducted in two scenarios: one using a dataset that included 12 variables, and another in which the variables were reduced to those most significantly correlated with cardiovascular disease, i.e., 4 variables; both scenarios with 918 clinical records per variable. Using the Neutrosophic AHP-TOPSIS method for model selection, the CNN model trained with the original dataset was identified as the best-performing model among the proposed options. In specific terms, the evaluation metrics of the CNN model were as follows: an accuracy of 92.17%, a sensitivity of 94.51%, a specificity of 90.78%, an F1-Score of 93.30%, and an area under the ROC curve of 90.03%. |
| format | Article |
| id | doaj-art-67cba5e6805f41de82592807cb7930f7 |
| institution | Kabale University |
| issn | 2331-6055 2331-608X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | University of New Mexico |
| record_format | Article |
| series | Neutrosophic Sets and Systems |
| spelling | doaj-art-67cba5e6805f41de82592807cb7930f72025-08-22T11:00:41ZengUniversity of New MexicoNeutrosophic Sets and Systems2331-60552331-608X2024-12-017421022610.5281/zenodo.14418335Detection of Cardiovascular Diseases Using Predictive Models Based on Deep Learning Techniques: A Hybrid Neutrosophic AHP-TOPSIS Approach for Model SelectionJulio Barzola-MontesesRosangela Caicedo-QuirozFranklin Parrales-BravoCristhian Medina-SuarezWendy Yanez-PazminoDavid Zabala-BlancoMaikel Y. Leyva-VazquezIn Ecuador and globally, cardiovascular diseases are the leading cause of mortality, accounting for a worrying 26.49% of deaths in 2019. An approach based on deep learning is applied to improve the capacity for early prediction and reduce its incidence. In this work, three different models were proposed and compared: deep neural networks (DNN), convolutional neural networks (CNN), and multilayer perceptron (MLP). Experiments were conducted in two scenarios: one using a dataset that included 12 variables, and another in which the variables were reduced to those most significantly correlated with cardiovascular disease, i.e., 4 variables; both scenarios with 918 clinical records per variable. Using the Neutrosophic AHP-TOPSIS method for model selection, the CNN model trained with the original dataset was identified as the best-performing model among the proposed options. In specific terms, the evaluation metrics of the CNN model were as follows: an accuracy of 92.17%, a sensitivity of 94.51%, a specificity of 90.78%, an F1-Score of 93.30%, and an area under the ROC curve of 90.03%.https://fs.unm.edu/NSS/17CardiovascularDiseases.pdfheart diseasepredictionconvolutional neural networkdeep neural networkmultilayer perceptronneutrosophic ahp-topsis |
| spellingShingle | Julio Barzola-Monteses Rosangela Caicedo-Quiroz Franklin Parrales-Bravo Cristhian Medina-Suarez Wendy Yanez-Pazmino David Zabala-Blanco Maikel Y. Leyva-Vazquez Detection of Cardiovascular Diseases Using Predictive Models Based on Deep Learning Techniques: A Hybrid Neutrosophic AHP-TOPSIS Approach for Model Selection Neutrosophic Sets and Systems heart disease prediction convolutional neural network deep neural network multilayer perceptron neutrosophic ahp-topsis |
| title | Detection of Cardiovascular Diseases Using Predictive Models Based on Deep Learning Techniques: A Hybrid Neutrosophic AHP-TOPSIS Approach for Model Selection |
| title_full | Detection of Cardiovascular Diseases Using Predictive Models Based on Deep Learning Techniques: A Hybrid Neutrosophic AHP-TOPSIS Approach for Model Selection |
| title_fullStr | Detection of Cardiovascular Diseases Using Predictive Models Based on Deep Learning Techniques: A Hybrid Neutrosophic AHP-TOPSIS Approach for Model Selection |
| title_full_unstemmed | Detection of Cardiovascular Diseases Using Predictive Models Based on Deep Learning Techniques: A Hybrid Neutrosophic AHP-TOPSIS Approach for Model Selection |
| title_short | Detection of Cardiovascular Diseases Using Predictive Models Based on Deep Learning Techniques: A Hybrid Neutrosophic AHP-TOPSIS Approach for Model Selection |
| title_sort | detection of cardiovascular diseases using predictive models based on deep learning techniques a hybrid neutrosophic ahp topsis approach for model selection |
| topic | heart disease prediction convolutional neural network deep neural network multilayer perceptron neutrosophic ahp-topsis |
| url | https://fs.unm.edu/NSS/17CardiovascularDiseases.pdf |
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