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Utilizing a Two-Stage Taguchi Method and Artificial Neural Network for the Precise Forecasting of Cardiovascular Disease Risk
The complexity of cardiovascular disease onset emphasizes the vital role of early detection in prevention. This study aims to enhance disease prediction accuracy using personal devices, aligning with point-of-care testing (POCT) objectives. This study introduces a two-stage Taguchi optimization (TST...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669281/ https://www.ncbi.nlm.nih.gov/pubmed/38002410 http://dx.doi.org/10.3390/bioengineering10111286 |
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author | Lin, Chia-Ming Lin, Yu-Shiang |
author_facet | Lin, Chia-Ming Lin, Yu-Shiang |
author_sort | Lin, Chia-Ming |
collection | PubMed |
description | The complexity of cardiovascular disease onset emphasizes the vital role of early detection in prevention. This study aims to enhance disease prediction accuracy using personal devices, aligning with point-of-care testing (POCT) objectives. This study introduces a two-stage Taguchi optimization (TSTO) method to boost predictive accuracy in an artificial neural network (ANN) model while minimizing computational costs. In the first stage, optimal hyperparameter levels and trends were identified. The second stage determined the best settings for the ANN model’s hyperparameters. In this study, we applied the proposed TSTO method with a personal computer to the Kaggle Cardiovascular Disease dataset. Subsequently, we identified the best setting for the hyperparameters of the ANN model, setting the hidden layer to 4, activation function to tanh, optimizer to SGD, learning rate to 0.25, momentum rate to 0.85, and hidden nodes to 10. This setting led to a state-of-the-art accuracy of 74.14% in predicting the risk of cardiovascular disease. Moreover, the proposed TSTO method significantly reduced the number of experiments by a factor of 40.5 compared to the traditional grid search method. The TSTO method accurately predicts cardiovascular risk and conserves computational resources. It is adaptable for low-power devices, aiding the goal of POCT. |
format | Online Article Text |
id | pubmed-10669281 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106692812023-11-04 Utilizing a Two-Stage Taguchi Method and Artificial Neural Network for the Precise Forecasting of Cardiovascular Disease Risk Lin, Chia-Ming Lin, Yu-Shiang Bioengineering (Basel) Article The complexity of cardiovascular disease onset emphasizes the vital role of early detection in prevention. This study aims to enhance disease prediction accuracy using personal devices, aligning with point-of-care testing (POCT) objectives. This study introduces a two-stage Taguchi optimization (TSTO) method to boost predictive accuracy in an artificial neural network (ANN) model while minimizing computational costs. In the first stage, optimal hyperparameter levels and trends were identified. The second stage determined the best settings for the ANN model’s hyperparameters. In this study, we applied the proposed TSTO method with a personal computer to the Kaggle Cardiovascular Disease dataset. Subsequently, we identified the best setting for the hyperparameters of the ANN model, setting the hidden layer to 4, activation function to tanh, optimizer to SGD, learning rate to 0.25, momentum rate to 0.85, and hidden nodes to 10. This setting led to a state-of-the-art accuracy of 74.14% in predicting the risk of cardiovascular disease. Moreover, the proposed TSTO method significantly reduced the number of experiments by a factor of 40.5 compared to the traditional grid search method. The TSTO method accurately predicts cardiovascular risk and conserves computational resources. It is adaptable for low-power devices, aiding the goal of POCT. MDPI 2023-11-04 /pmc/articles/PMC10669281/ /pubmed/38002410 http://dx.doi.org/10.3390/bioengineering10111286 Text en © 2023 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 Lin, Chia-Ming Lin, Yu-Shiang Utilizing a Two-Stage Taguchi Method and Artificial Neural Network for the Precise Forecasting of Cardiovascular Disease Risk |
title | Utilizing a Two-Stage Taguchi Method and Artificial Neural Network for the Precise Forecasting of Cardiovascular Disease Risk |
title_full | Utilizing a Two-Stage Taguchi Method and Artificial Neural Network for the Precise Forecasting of Cardiovascular Disease Risk |
title_fullStr | Utilizing a Two-Stage Taguchi Method and Artificial Neural Network for the Precise Forecasting of Cardiovascular Disease Risk |
title_full_unstemmed | Utilizing a Two-Stage Taguchi Method and Artificial Neural Network for the Precise Forecasting of Cardiovascular Disease Risk |
title_short | Utilizing a Two-Stage Taguchi Method and Artificial Neural Network for the Precise Forecasting of Cardiovascular Disease Risk |
title_sort | utilizing a two-stage taguchi method and artificial neural network for the precise forecasting of cardiovascular disease risk |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669281/ https://www.ncbi.nlm.nih.gov/pubmed/38002410 http://dx.doi.org/10.3390/bioengineering10111286 |
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