Glucose prediction data analytics for diabetic patients monitoring

Huzooree, Geshwaree; Khedo, Kavi Kumar; Joonas, Noorjehan

doi:10.1109/nextcomp.2017.8016197

Cited by 13 publications

(2 citation statements)

References 27 publications

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“…This model introduced new attributes such as diabetic ketoacidosis, swelling, infection years, and diabetic septic foot were found to be significant. These attributes were not included in the previously mentioned studies [30]; [13]; [31]; [9]. Also, in this paper, we investigated five performance metrics such as F1 and recall.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Analysis of Machine Learning Algorithms to Build a Predictive Model for Detecting Diabetes Complications

Abaker

Saeed

2021

IJCAI

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Section: Literature Reviewmentioning

confidence: 99%

A Comparative Analysis of Machine Learning Algorithms to Build a Predictive Model for Detecting Diabetes Complications

Abaker

Saeed

2021

IJCAI

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“…For examples, a fusion of the linear regression model and the support vector machine model [4] was developed for performing the blood glucose estimation using the diabetes dataset. Moreover, an autoregressive (ARX) model [5] was proposed for handling the various exogenous inputs. The computer numerical simulation results showed that the algorithm can improve the accuracy of the blood glucose estimation.…”

Section: Introductionmentioning

confidence: 99%

Fusion of multi-regression models based on the histogram information for blood glucose estimation

Wei

Guo

Ling

et al. 2023

Preprint

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Diabetes is a chronic disease that severely degrades the human health. Hence, the blood glucose estimation plays an important role for monitoring the diabetic condition. In order to better estimate the blood glucose values, the multi-regression models are employed. It is worth noting that increasing the total number of the regression models would decrease the regression error. Therefore, this paper proposes a method for fusing the various regression models together based on the histogram information of the blood glucose values in the training set. The computer numerical simulation results show that the regression error yielded by our proposed method is significantly lower than those yielded by the existing methods. Also, our proposed method is also applicable for other regression applications.

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Hereditary Factor‐Based Multi‐Featured Algorithm for Early DiabetesDetection Using Machine Learning

Deepajothi

Juliana²,

Aruna

et al. 2022

Artificial Intelligent Techniques for Wireless Communication and Networking

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Glucose prediction data analytics for diabetic patients monitoring

Cited by 13 publications

References 27 publications

A Comparative Analysis of Machine Learning Algorithms to Build a Predictive Model for Detecting Diabetes Complications

A Comparative Analysis of Machine Learning Algorithms to Build a Predictive Model for Detecting Diabetes Complications

Fusion of multi-regression models based on the histogram information for blood glucose estimation

Hereditary Factor‐Based Multi‐Featured Algorithm for Early DiabetesDetection Using Machine Learning

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