Blood glucose concentration prediction based on kernel canonical correlation analysis with particle swarm optimization and error compensation

He, Jinli; Wang, Youqing

doi:10.1016/j.cmpb.2020.105574

Cited by 12 publications

(5 citation statements)

References 28 publications

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“…However, these models are limited by their inability to capture the complex relationships between the variables that influence blood glucose levels. In 2020, Jinli He et al [5]. proposed a method for blood glucose concentration prediction called Kernel Canonical Correlation Analysis with Particle Swarm Optimization and Error Compensation.…”

Section: Related Workmentioning

confidence: 99%

“…Their approach utilized only historical blood glucose data as input, instead of complex multi-dimensional inputs. Previous studies have shown that Canonical Correlation Analysis (CCA) can effectively predict blood glucose, but only considering the linear relationship between historical blood glucose values and predicted values is regrettable [6]. To address this limitation, the authors introduced a kernel function to identify the non-linear relationship between blood glucose.…”

Section: Related Workmentioning

confidence: 99%

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Prediction and Analysis of Blood Glucose Levels based on Tabnet

Yang¹

2023

SJT

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Background: Blood glucose level prediction plays a significant role in the management of diabetes. Accurate prediction of blood glucose levels helps patients and doctors to make informed decisions regarding diet, exercise, and medication. The use of machine learning algorithms for blood glucose prediction has gained attention in recent years. Tabnet is one such algorithm that has shown promising results in various prediction tasks. Aim: The aim of this study is to evaluate the performance of Tabnet for blood glucose level prediction and compare it with other commonly used algorithms, including LR, DT, SVM, RF, and EN. Methods: A dataset of blood glucose levels of diabetic patients was used for this study. The dataset was preprocessed, and features were selected using correlation-based feature selection. Tabnet and other algorithms were trained on the dataset using 5-fold cross-validation. The performance of each algorithm was evaluated using root mean squared error (RMSE) and mean squared error (MSE). Results: The experimental results showed that Tabnet performed the best in terms of RMSE and MSE, with values of 0.5097 and 0.2523, respectively. The LR algorithm had an RMSE of 0.5126 and an MSE of 0.2629, while the DT algorithm had an RMSE of 0.7543 and an MSE of 0.5689. The SVM algorithm had an RMSE of 0.5165 and an MSE of 0.2663, while the RF algorithm had an RMSE of 0.5188 and an MSE of 0.2691. The EN algorithm had an RMSE of 0.5547 and an MSE of 0.3077. Conclusion: In this study, Tabnet was found to be the best algorithm for blood glucose level prediction compared to other commonly used algorithms. The results demonstrate the potential of Tabnet for predicting blood glucose levels in diabetic patients, which can assist in effective diabetes management.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Prediction and Analysis of Blood Glucose Levels based on Tabnet

Yang¹

2023

SJT

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“…MAPE calculates the average error rate for the correct values and MAE is the mean of the absolute difference between the observed and predicted values [59]. Based on recent studies on the application of time series forecasting, the R 2 determination coefficient is a promising way to assess model performance [60,61].…”

Section: Algorithm Evaluationmentioning

confidence: 99%

Long short-term memory stacking model to predict the number of cases and deaths caused by COVID-19

Fernandes

Stefenon

Seman

et al. 2022

IFS

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The long short-term memory (LSTM) is a high-efficiency model for forecasting time series, for being able to deal with a large volume of data from a time series with nonlinearities. As a case study, the stacked LSTM will be used to forecast the growth of the pandemic of COVID-19, based on the increase in the number of contaminated and deaths in the State of Santa Catarina, Brazil. COVID-19 has been spreading very quickly, causing great concern in relation to the ability to care for critically ill patients. Control measures are being imposed by governments with the aim of reducing the contamination and the spreading of viruses. The forecast of the number of contaminated and deaths caused by COVID-19 can help decision making regarding the adopted restrictions, making them more or less rigid depending on the pandemic’s control capacity. The use of LSTM stacking shows an R2 of 0.9625 for confirmed cases and 0.9656 for confirmed deaths caused by COVID-19, being superior to the combinations among other evaluated models.

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“…The ECG classification accuracy of the model based on adversarial domain adaptation reaches 92.3% [26]. Some scholars found that the classification effect of the support vector machine method is poor, the training of linear discriminant analysis is easy to overfit, and the training time of deep learning algorithms is too long [27][28][29]. Scholars' study shows that building a back-propagation neural network (BPNN) model to classify AECG, the classification accuracy is only 72.27% [30], is not suitable for detecting cardiovascular disease as the CNN.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we mainly aimed to explore an accurate de-noising and feature extraction method of ECG based on a wavelet and perform intelligent modeling to classify AECG based on PSO optimized BPNN with combining the advantage of BPNN and PSO. With the consideration of the training time of deep learning algorithms is too long [27][28][29], the feature dimension reduction are also under consideration to reduce the complexity of the model to save class time and up accuracy.…”

Section: Introductionmentioning

confidence: 99%

A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification

Zhang

et al. 2021

BMC Med Inform Decis Mak

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Background As proven to reflect the work state of heart and physiological situation objectively, electrocardiogram (ECG) is widely used in the assessment of human health, especially the diagnosis of heart disease. The accuracy and reliability of abnormal ECG (AECG) decision depend to a large extent on the feature extraction. However, it is often uneasy or even impossible to obtain accurate features, as the detection process of ECG is easily disturbed by the external environment. And AECG got many species and great variation. What’s more, the ECG result obtained after a long time past, which can not reach the purpose of early warning or real-time disease diagnosis. Therefore, developing an intelligent classification model with an accurate feature extraction method to identify AECG is of quite significance. This study aimed to explore an accurate feature extraction method of ECG and establish a suitable model for identifying AECG and the diagnosis of heart disease. Methods In this research, the wavelet combined with four operations and adaptive threshold methods were applied to filter the ECG and extract its feature waves first. Then, a BP neural network (BPNN) intelligent model and a particle swarm optimization (PSO) improved BPNN (PSO-BPNN) intelligent model based on MIT-BIH open database was established to identify ECG. To reduce the complexity of the model, the principal component analysis (PCA) was used to minimize the feature dimension. Results Wavelet transforms combined four operations and adaptive threshold methods were capable of ECG filtering and feature extraction. PCA can significantly deduce the modeling feature dimension to minimize the complexity and save classification time. The PSO-BPNN intelligent model was suitable for identifying five types of ECG and showed better effects while comparing it with the BPNN model. Conclusion In summary, it was further concluded that the PSO-BPNN intelligent model would be a suitable way to identify AECG and provide a tool for the diagnosis of heart disease.

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Blood glucose concentration prediction based on kernel canonical correlation analysis with particle swarm optimization and error compensation

Cited by 12 publications

References 28 publications

Prediction and Analysis of Blood Glucose Levels based on Tabnet

Prediction and Analysis of Blood Glucose Levels based on Tabnet

Long short-term memory stacking model to predict the number of cases and deaths caused by COVID-19

A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification

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