On the development of diagnostic support algorithms based on CPET biosignals data via machine learning and wavelets
For preventing health complications and reducing the strain on healthcare systems, early identification of diseases is imperative. In this context, artificial intelligence has become increasingly prominent in the field of medicine, offering essential support for disease diagnosis. This article intro...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-01-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2474.pdf |
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| author | Rafael F. Pinheiro Rui Fonseca-Pinto |
| author_facet | Rafael F. Pinheiro Rui Fonseca-Pinto |
| author_sort | Rafael F. Pinheiro |
| collection | DOAJ |
| description | For preventing health complications and reducing the strain on healthcare systems, early identification of diseases is imperative. In this context, artificial intelligence has become increasingly prominent in the field of medicine, offering essential support for disease diagnosis. This article introduces an algorithm that builds upon an earlier methodology to assess biosignals acquired through cardiopulmonary exercise testing (CPET) for identifying metabolic syndrome (MS), heart failure (HF), and healthy individuals (H). Leveraging support vector machine (SVM) technology, a well-known machine learning classification method, in combination with wavelet transforms for feature extraction, the algorithm takes an innovative approach. The model was trained on CPET data from 45 participants, including 15 with MS, 15 with HF, and 15 healthy controls. For binary classification tasks, the SVM with a polynomial kernel and 5-level wavelet transform (SVM-POL-BW5) outperformed similar methods described in the literature. Moreover, one of the main contributions of this study is the development of a multi-class classification algorithm using the SVM employing a linear kernel and 3-level wavelet transforms (SVM-LIN-MW3), reaching an average accuracy of 95%. In conclusion, the application of SVM-based algorithms combined with wavelet transforms to analyze CPET data shows promise in diagnosing various diseases, highlighting their adaptability and broader potential applications in healthcare. |
| format | Article |
| id | doaj-art-aaa07cb86f2348dba8b78285249a1acf |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-aaa07cb86f2348dba8b78285249a1acf2025-02-01T15:05:19ZengPeerJ Inc.PeerJ Computer Science2376-59922025-01-0111e247410.7717/peerj-cs.2474On the development of diagnostic support algorithms based on CPET biosignals data via machine learning and waveletsRafael F. PinheiroRui Fonseca-PintoFor preventing health complications and reducing the strain on healthcare systems, early identification of diseases is imperative. In this context, artificial intelligence has become increasingly prominent in the field of medicine, offering essential support for disease diagnosis. This article introduces an algorithm that builds upon an earlier methodology to assess biosignals acquired through cardiopulmonary exercise testing (CPET) for identifying metabolic syndrome (MS), heart failure (HF), and healthy individuals (H). Leveraging support vector machine (SVM) technology, a well-known machine learning classification method, in combination with wavelet transforms for feature extraction, the algorithm takes an innovative approach. The model was trained on CPET data from 45 participants, including 15 with MS, 15 with HF, and 15 healthy controls. For binary classification tasks, the SVM with a polynomial kernel and 5-level wavelet transform (SVM-POL-BW5) outperformed similar methods described in the literature. Moreover, one of the main contributions of this study is the development of a multi-class classification algorithm using the SVM employing a linear kernel and 3-level wavelet transforms (SVM-LIN-MW3), reaching an average accuracy of 95%. In conclusion, the application of SVM-based algorithms combined with wavelet transforms to analyze CPET data shows promise in diagnosing various diseases, highlighting their adaptability and broader potential applications in healthcare.https://peerj.com/articles/cs-2474.pdfCPETMulti-class classificationEarly diagnosis systemsHeart diseaseMetabolic diseases |
| spellingShingle | Rafael F. Pinheiro Rui Fonseca-Pinto On the development of diagnostic support algorithms based on CPET biosignals data via machine learning and wavelets PeerJ Computer Science CPET Multi-class classification Early diagnosis systems Heart disease Metabolic diseases |
| title | On the development of diagnostic support algorithms based on CPET biosignals data via machine learning and wavelets |
| title_full | On the development of diagnostic support algorithms based on CPET biosignals data via machine learning and wavelets |
| title_fullStr | On the development of diagnostic support algorithms based on CPET biosignals data via machine learning and wavelets |
| title_full_unstemmed | On the development of diagnostic support algorithms based on CPET biosignals data via machine learning and wavelets |
| title_short | On the development of diagnostic support algorithms based on CPET biosignals data via machine learning and wavelets |
| title_sort | on the development of diagnostic support algorithms based on cpet biosignals data via machine learning and wavelets |
| topic | CPET Multi-class classification Early diagnosis systems Heart disease Metabolic diseases |
| url | https://peerj.com/articles/cs-2474.pdf |
| work_keys_str_mv | AT rafaelfpinheiro onthedevelopmentofdiagnosticsupportalgorithmsbasedoncpetbiosignalsdataviamachinelearningandwavelets AT ruifonsecapinto onthedevelopmentofdiagnosticsupportalgorithmsbasedoncpetbiosignalsdataviamachinelearningandwavelets |