Blood Glucose Concentration Prediction Based on Canonical Correlation Analysis

He, Jinli; He, Tong Chuan; Wang, Youqing

doi:10.23919/chicc.2019.8865767

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…Therefore, researchers [19][20][21] began to attempt to establish a prediction model based on the time series to predict the trend of tunnel structural deformation. He et al [22] used the regression method to analyze the relationship between tunnel convergence value and time for the 55 phase transverse convergence deformation data of a section of highway tunnel, and the results show that the tunnel convergence value and time are nonlinear logarithmic. Xie et al [23] used the ARMA time-series model to model and analyze the measured data of 30 monitoring points of a subway tunnel in Nanjing during an operation period, so as to achieve short-term deformation prediction.…”

Section: Time-series Prediction Modelsmentioning

confidence: 99%

Comparison of ARIMA and LSTM in Predicting Structural Deformation of Tunnels during Operation Period

Duan

Zhang

2023

Data

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Accurately predicting the structural deformation trend of tunnels during operation is significant to improve the scientificity of tunnel safety maintenance. With the development of data science, structural deformation prediction methods based on time-series data have attracted attention. Auto Regressive Integrated Moving Average model (ARIMA) is a classical statistical analysis model, which is suitable for processing non-stationary time-series data. Long- and Short-Term Memory (LSTM) is a special cyclic neural network that can learn long-term dependent information in time series. Both are widely used in the field of temporal prediction. In view of the lack of time-series prediction in the tunnel deformation field, the body of this paper uses historical data of the Xinjian Road and the Dalian Road tunnel in Shanghai to propose a new way of modeling based on single points and road sections. ARIMA and LSTM models are applied in comprehensive experiments, and the results show that: (1) Both LSTM and ARIMA models have great performance for settlement and convergence deformation. (2) The overall robustness of ARIMA is better than that of LSTM, and it is more adaptable to the datasets. (3) The model prediction performance is closely related to the data quality. ARIMA has more stable performance under the lack of data volume, while LSTM has better performance with high-quality data and higher upper limit.

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Section: Time-series Prediction Modelsmentioning

confidence: 99%

Comparison of ARIMA and LSTM in Predicting Structural Deformation of Tunnels during Operation Period

Duan

Zhang

2023

Data

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“…It is relatively simple to measure a person's blood glucose under the existing medical conditions, but it takes a lot of human and material resources to detect the blood glucose of a large number of people in medical examination. Therefore, the prediction 2 of 16 of the blood glucose of a large number of people in medical examination by machine learning can save a lot of unnecessary expenses (for example [7]).…”

Section: Introductionmentioning

confidence: 99%

Application of Improved LightGBM Model in Blood Glucose Prediction

Wang

2020

Applied Sciences

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In recent years, with increasing social pressure and irregular schedules, many people have developed unhealthy eating habits, which has resulted in an increasing number of patients with diabetes, a disease that cannot be cured under the current medical conditions, and can only be mitigated by early detection and prevention. A lot of human and material resources are required for the detection of the blood glucose of a large number of people in medical examination, while the integrated learning model based on machine learning can quickly predict the blood glucose level and assist doctors in treatment. Therefore, an improved LightGBM model based on the Bayesian hyper-parameter optimization algorithm is proposed for the prediction of blood glucose, namely HY_LightGBM, which optimizes parameters using a Bayesian hyper-parameter optimization algorithm based on LightGBM. The Bayesian hyper-parameter optimization algorithm is a model-based method for finding the minimum value of the function so as to obtain the optimal parameters of the LightGBM model. Experiments have demonstrated that the parameters obtained by the Bayesian hyper-parameter optimization algorithm are superior to those obtained by a genetic algorithm and random search. The improved LightGBM model based on the Bayesian hyper-parameter optimization algorithm achieves a mean square error of 0.5961 in blood glucose prediction, with a higher accuracy than the XGBoost model and CatBoost model.

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“…He. et al in [5] gave a prediction on glucose concentration in blood, a primary factor leading to diabetes using Canonical correlation or Canonical variants analysis. The historical blood glucose data of patients and the future blood glucose data were modeled by canonical correlation analysis.…”

Section: Introductionmentioning

confidence: 99%

Diabetes Prediction and Validation Model Using Ml Classification Algorithms

Dey¹

2020

ijarcs

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Diabetes is now a global wide concern, which can critically impact and disrupt the normal lifestyle and the everyday activities of any individual. Due to the lack of insulin and high glucose content in the body, anyone can get diagnosed with diabetes. Apart from all the medical factors, there are few additional non-medical factors in an individual's daily life like hypertension, heredity, daily standard activity, smoking habits, body mass index etc. that might play a part in triggering diabetes. Several medical studies reveal that for women sometimes pregnancy frequencies or any kind of heart issues can also trigger diabetes. The paper aims to predict the most critical factor that contributes in triggering diabetes in any individual by using classification and predictive analysis algorithms. Five well known machine learning classification algorithms are used where a filtering scheme based on 75% threshold accuracy rate is employed followed by verification using AUROC metric aiming low error rate and high prediction accuracy. Additionally, the model used Ensemble learning to make predictions and validates the proposed scheme against PIMA Indian Diabetes dataset.

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Blood Glucose Concentration Prediction Based on Canonical Correlation Analysis

Cited by 6 publications

References 17 publications

Comparison of ARIMA and LSTM in Predicting Structural Deformation of Tunnels during Operation Period

Comparison of ARIMA and LSTM in Predicting Structural Deformation of Tunnels during Operation Period

Application of Improved LightGBM Model in Blood Glucose Prediction

Diabetes Prediction and Validation Model Using Ml Classification Algorithms

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