Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/84865
Título: | A review of machine learning methods recently applied to FTIR spectroscopy data for the analysis of human blood cells |
Autor(es): | Fadlelmoula, Ahmed Catarino, Susana Oliveira Minas, Graça Carvalho, Vítor |
Palavras-chave: | FTIR spectroscopy Human blood cells Machine learning Review |
Data: | 29-Mai-2023 |
Editora: | MDPI |
Revista: | Micromachines |
Citação: | Fadlelmoula, A.; Catarino, S.O.; Minas, G.; Carvalho, V. A review of machine learning methods recently applied to FTIR spectroscopy data for the analysis of human blood cells. Micromachines 2023, 14, 1145. https://doi.org/10.3390/mi14061145 |
Resumo(s): | Machine learning (ML) is a broad term encompassing several methods that allow us to learn from data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient–provider decision-making. This paper presents a review of articles that discuss the use of Fourier transform infrared (FTIR) spectroscopy and ML for human blood analysis between the years 2019–2023. The literature review was conducted to identify published research of employed ML linked with FTIR for distinction between pathological and healthy human blood cells. The articles’ search strategy was implemented and studies meeting the eligibility criteria were evaluated. Relevant data related to the study design, statistical methods, and strengths and limitations were identified. A total of 39 publications in the last 5 years (2019–2023) were identified and evaluated for this review. Diverse methods, statistical packages, and approaches were used across the identified studies. The most common methods included support vector machine (SVM) and principal component analysis (PCA) approaches. Most studies applied internal validation and employed more than one algorithm, while only four studies applied one ML algorithm to the data. A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of ML methods. There is a need to ensure that multiple ML approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that the discrimination of human blood cells is being made with the highest efficient evidence. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/84865 |
DOI: | 10.3390/mi14061145 |
Versão da editora: | https://www.mdpi.com/2072-666X/14/6/1145 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CMEMS - Artigos em revistas internacionais/Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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micromachines-14-01145.pdf | 1,84 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons