Explainable machine learning for sleep apnea prediction

Troncoso-García, A. R.; Martínez-Ballesteros, M.; Martínez‐Álvarez, Francisco; Troncoso, Alicia

doi:10.1016/j.procs.2022.09.351

Cited by 17 publications

(4 citation statements)

References 15 publications

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“…The same was used in many papers. Further, the LSTM model was compared with different data sets, and its accuracy will be measured [12][13][14][15].…”

Section: Discussionmentioning

confidence: 99%

A Comprehensive Analysis of Deep Learning Models in Agricultural Crop Yield Prediction

Krithika S,

Sangeetha T A

2024

Int Res J Adv Engg Hub

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In comparison to machine learning algorithms, deep learning algorithms are more effective in terms of their ability to make accurate predictions about agricultural data. When trying to attain better outcomes, the accuracy and efficiency of the algorithm are highly crucial factors to consider. In order to help the farmers to accurately predict the yields of their crops, the study of agricultural data is of great assistance. This resulted in a significant amount of study, and the researchers used a variety of data to make predictions on the yields of the various crops. The primary objective of this study is to locate a more effective deep learning algorithm that may be used to forecast crop production. Hence the SLR was carried out and finally identified 20 opted research articles related to deep learning models utilized in agricultural data prediction. As a result, this SLR discovered that the LSTM-based deep learning models provide more accurate predictions when applied to agricultural data sets. A great number of research papers made use of the same model for better results.

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“…The same was used in many papers. Further, the LSTM model was compared with different data sets, and its accuracy will be measured [12][13][14][15].…”

Section: Discussionmentioning

confidence: 99%

A Comprehensive Analysis of Deep Learning Models in Agricultural Crop Yield Prediction

Krithika S,

Sangeetha T A

2024

Int Res J Adv Engg Hub

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“…LIME explainable ability is evaluated in [19]. A LIME use case is presented in [20], where LIME is applied as a way of adding explainability to a sleep apnea prediction method. A similar approach is SHapely Additive exPlanation (SHAP) [21].…”

Section: Post-hoc Xaimentioning

confidence: 99%

A new approach based on association rules to add explainability to time series forecasting models

et al. 2023

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“…Next, we will discuss a few other approaches. In [181], the authors used the LIME library [182] on top of their model for interpretability. In [127], the authors proposed something similar to the layout in [68] for model explainability.…”

Section: Interpretabilitymentioning

confidence: 99%

Advances in Modeling and Interpretability of Deep Neural Sleep Staging: A Systematic Review

Soleimani,

Barahona,

Chen

et al. 2023

Physiologia

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Sleep staging has a very important role in diagnosing patients with sleep disorders. In general, this task is very time-consuming for physicians to perform. Deep learning shows great potential to automate this process and remove physician bias from decision making. In this study, we aim to identify recent trends on performance improvement and the causes for these trends. Recent papers on sleep stage classification and interpretability are investigated to explore different modeling and data manipulation techniques, their efficiency, and recent advances. We identify an improvement in performance up to 12% on standard datasets over the last 5 years. The improvements in performance do not appear to be necessarily correlated to the size of the models, but instead seem to be caused by incorporating new architectural components, such as the use of transformers and contrastive learning.

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Explainable machine learning for sleep apnea prediction

Cited by 17 publications

References 15 publications

A Comprehensive Analysis of Deep Learning Models in Agricultural Crop Yield Prediction

A Comprehensive Analysis of Deep Learning Models in Agricultural Crop Yield Prediction

A new approach based on association rules to add explainability to time series forecasting models

Advances in Modeling and Interpretability of Deep Neural Sleep Staging: A Systematic Review

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