Predictive analytics for blood glucose concentration: an empirical study using the tree-based ensemble approach

Liu, Jiaming; Wang, Liuan; Zhang, Linan; Zhang, Zeming; Zhang, Sicheng

doi:10.1108/lht-08-2019-0171

Cited by 9 publications

(5 citation statements)

References 42 publications

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“…Zhou B [ 30 ] improved the weight update rule of the Boosting algorithm and introduced a misclassification cost mechanism to improve the accuracy. Liu J [ 31 ] proposed the random forest algorithm with weights, introduced weighting techniques in the construction of decision trees, and used voting with weights in the decision process to improve the prediction ability. The feature selection method represents the objects in the original dataset with a subset of features and removes redundant feature information [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction and optimization of employee turnover intentions in enterprises based on unbalanced data

Li,

Fox

2023

PLoS ONE

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The sudden resignation of core employees often brings losses to companies in various aspects. Traditional employee turnover theory cannot analyze the unbalanced data of employees comprehensively, which leads the company to make wrong decisions. In the face the classification of unbalanced data, the traditional Support Vector Machine (SVM) suffers from insufficient decision plane offset and unbalanced support vector distribution, for which the Synthetic Minority Oversampling Technique (SMOTE) is introduced to improve the balance of generated data. Further, the Fuzzy C-mean (FCM) clustering is improved and combined with the SMOTE (IFCM-SMOTE-SVM) to new synthesized samples with higher accuracy, solving the drawback that the separation data synthesized by SMOTE is too random and easy to generate noisy data. The kernel function is combined with IFCM-SMOTE-SVM and transformed to a high-dimensional space for clustering sampling and classification, and the kernel space-based classification algorithm (KS-IFCM-SMOTE-SVM) is proposed, which improves the effectiveness of the generated data on SVM classification results. Finally, the generalization ability of KS-IFCM-SMOTE-SVM for different types of enterprise data is experimentally demonstrated, and it is verified that the proposed algorithm has stable and accurate performance. This study introduces the SMOTE and FCM clustering, and improves the SVM by combining the data transformation in the kernel space to achieve accurate classification of unbalanced data of employees, which helps enterprises to predict whether employees have the tendency to leave in advance.

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Section: Introductionmentioning

confidence: 99%

Prediction and optimization of employee turnover intentions in enterprises based on unbalanced data

Li,

Fox

2023

PLoS ONE

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“…It can be inferred that,the dataset has given RMSE values in the range of 54 to 65 for the tested existing models, while the optimised Regression model has given RMSE of 1.5 units, which shows the supremacy of the method on the dataset. Papers in literature such as [15], [18] which employ individual models such as KNN, SVR and article [26], study in [27], paper [28] and research work [29], which handle ensembles, perform well with minimum error with respect to classifying diabetic and non-diabetic outcomes and in predicting short term diabetic information. But these learners, when used for the diabetic progression dataset have not so well in comparison with the proposed technique.…”

Section: Resultsmentioning

confidence: 99%

“…The ensemble has obtained 90% classification accuracy for bagging and boosting methods, with Logistic Model Tree (LMT) as the base model, taking AUC as the performance metric. An empirical study using various tree based ensemble models have been proposed in [29], which discusses performance of Adaboost, GBDT, RF and Bagging techniques, resulting in least RMSE of 0.005 with Adaboost technique compared to other ensembles.…”

Section: A Related Workmentioning

confidence: 99%

An Optimised Multivariable Regression Model for Predictive Analysis of Diabetic Disease Progression

Daliya

Ramesh

2021

IEEE Access

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With the advent of smart systems and smart IoT network all over the world leading to enormous amount of data generation; the right analysis and decision making based on the relevant data plays a crucial role. Various industries such as transportation, retail, healthcare etc. rely on analysis using this huge volumes of data for intelligent decision making. In smart healthcare system, accurate analysis of patients' data and prediction of diseases and medicine is important. To a great extent, fatalities can be avoided by timely recommendation of healthcare measures and immediate alert on emergency conditions. The use of machine learning algorithm for precise predictive analysis of data can be very promising in the field of healthcare. In this paper, optimised Multivariable Linear regression method is used to predict the diabetic disease progression of 442 patients based on various parameters such as age, gender, Body Mass Index and 6 different blood serum measurements. Here optimisation is performed using feature reduction and logarithmic transformation. The predicted output is found to be closely associated with actual output data with a Root Mean Square Error of 1.5 units; which indicates higher accuracy in comparison with the non-optimised model with the error of 54 units. There has also been a comparison with the results obtained from other state of the art regression methods, which proves that the proposed model exhibits maximum accuracy. This method can be used to provide promising medical advice to the patients on how to reduce the diabetic disease progression over a year by controlling various health parameters.

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“…Decision trees were used as prediction models for risk factor interactions in diabetes and to identify subjects with impaired glucose metabolism ( Ramezankhani et al, 2016 ; Hische et al, 2010 ). Different DT models have been used to identify vital indicators in relation to blood glucose prediction ( Liu et al, 2020 ). One study developed a non-invasive system to detect blood glucose levels based on the conservation-of-energy method and physiological parameters ( Zhang et al, 2017 ).…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection

Alhaddad

Aly

Gad

et al. 2022

Front. Bioeng. Biotechnol.

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Diabetes mellitus is characterized by elevated blood glucose levels, however patients with diabetes may also develop hypoglycemia due to treatment. There is an increasing demand for non-invasive blood glucose monitoring and trends detection amongst people with diabetes and healthy individuals, especially athletes. Wearable devices and non-invasive sensors for blood glucose monitoring have witnessed considerable advances. This review is an update on recent contributions utilizing novel sensing technologies over the past five years which include electrocardiogram, electromagnetic, bioimpedance, photoplethysmography, and acceleration measures as well as bodily fluid glucose sensors to monitor glucose and trend detection. We also review methods that use machine learning algorithms to predict blood glucose trends, especially for high risk events such as hypoglycemia. Convolutional and recurrent neural networks, support vector machines, and decision trees are examples of such machine learning algorithms. Finally, we address the key limitations and challenges of these studies and provide recommendations for future work.

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Predictive analytics for blood glucose concentration: an empirical study using the tree-based ensemble approach

Cited by 9 publications

References 42 publications

Prediction and optimization of employee turnover intentions in enterprises based on unbalanced data

Prediction and optimization of employee turnover intentions in enterprises based on unbalanced data

An Optimised Multivariable Regression Model for Predictive Analysis of Diabetic Disease Progression

Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection

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