Forecasting Basal Insulin for the Clinical Therapy of Juvenile Diabetes at Onset

Deja, Rafał; Froelich, Wojciech

doi:10.1016/j.procs.2022.09.046

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Cited by 3 publications

References 8 publications

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InsNET: Accurate Basal and Bolus Insulin Dose Prediction for Closed Loop Diabetes Management

Kalita,

Mirza

2023

2023 45th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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InsNET: Accurate Basal and Bolus Insulin Dose Prediction for Closed Loop Diabetes Management

Kalita,

Mirza

2023

2023 45th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Comparative Analysis of Time Series Forecasting using ARIMA, and GRNNs Models: A Case Study of Death Rate of Diabetic Mellitus in Canada

Khan,

Hyder Talpur,

Aslam

2024

VFAST trans. math.

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This research aims to compare ARIMA and GRNN models alone. For this comparison the death rate for diabetes mellitus time series data of Canada is used. Autoregressive Integrated Moving Average (ARIMA), and Generalized Regression Neural Networks (GRNN) models were applied for time series prediction of the death rate for diabetes mellitus—trained data for two models from 2000 to 2015. Test data was used to compare the precision through data from 2016 to 2021. The ARIMA model was applied using the auto-command through R package which provided the least BIC and AIC values. The mean absolute deviation (MAD), root mean squared error (RMSE), and mean absolute percentage error (MAPE) were employed to measure the forecasting efficiency of the models. The ARIMA model had the highest prediction accuracy as compared to the GRNN model. ARIMA predicts the death rate for diabetes mellitus more accurately and robustly compared to the GRNNs model.

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An Expert System for Insulin Dosage Prediction

Smt. A. Neethika,

K. V V S L Varshitha,

L. Naga Jahnavi

et al. 2024

IJARSCT

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Diabetes Mellitus is a pervasive metabolic disorder demanding precise blood glucose regulation to mitigate complications. Despite lifestyle adjustments, insulin dosage optimization remains pivotal for effective management. Our expert system employs Gradient Boosting Classifier for diabetes prediction and Logistic Regression for insulin dosage estimation, integrating patient data to automate decision-making. This approach facilitates proactive and personalized care, enhancing outcomes and representing a notable advancement in diabetes management. By leveraging machine learning techniques, this system accurately identifies diabetic patients and tailors insulin dosage recommendations based on individual characteristics and treatment history. Through automation, it streamlines decision-making processes, alleviating healthcare provider burden and empowering patients to actively participate in their care. Ultimately, our system promises to improve treatment efficacy and patient quality of life, heralding a new era in diabetes management

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Forecasting Basal Insulin for the Clinical Therapy of Juvenile Diabetes at Onset

Cited by 3 publications

References 8 publications

InsNET: Accurate Basal and Bolus Insulin Dose Prediction for Closed Loop Diabetes Management

InsNET: Accurate Basal and Bolus Insulin Dose Prediction for Closed Loop Diabetes Management

Comparative Analysis of Time Series Forecasting using ARIMA, and GRNNs Models: A Case Study of Death Rate of Diabetic Mellitus in Canada

An Expert System for Insulin Dosage Prediction

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