Artículo
Explainable machine learning for sleep apnea prediction
Autor/es | Troncoso García, Ángela del Robledo
Martínez Ballesteros, María del Mar Martínez Álvarez, Francisco Troncoso Lora, Alicia |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2022 |
Fecha de depósito | 2023-04-10 |
Publicado en |
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Resumen | Machine and deep learning has become one of the most useful tools in the last years as a diagnosis-decision-support tool in the health area. However, it is widely known that artificial intelligence models are considered a ... Machine and deep learning has become one of the most useful tools in the last years as a diagnosis-decision-support tool in the health area. However, it is widely known that artificial intelligence models are considered a black box and most experts experience difficulties explaining and interpreting the models and their results. In this context, explainable artificial intelligence is emerging with the aim of providing black-box models with sufficient interpretability so that models can be easily understood and further applied. Obstructive sleep apnea is a common chronic respiratory disease related to sleep. Its diagnosis nowadays is done by processing different data signals, such as electrocardiogram or respiratory rate. The waveform of the respiratory signal is of importance too. Machine learning models could be applied to the signal's analysis. Data from a polysomnography study for automatic sleep apnea detection have been used to evaluate the use of the Local Interpretable Model-Agnostic (LIME) library for explaining the health data models. Results obtained help to understand how several features have been used in the model and their influence in the quality of sleep. |
Agencias financiadoras | Ministerio de Ciencia e Innovación (MICIN). España Junta de Andalucía |
Identificador del proyecto | PID2020-117954RB-C21
PY20- 00870 UPO-138516 |
Cita | Troncoso García, Á.d.R., Martínez Ballesteros, M.d.M., Martínez Álvarez, F. y Troncoso Lora, A. (2022). Explainable machine learning for sleep apnea prediction. Procedia Computer Science, 207, 2924-2933. https://doi.org/10.1016/j.procs.2022.09.351. |
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