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A comprehensive review of machine learning techniques on diabetes detection
Diabetes mellitus has been an increasing concern owing to its high morbidity, and the
average age of individual affected by of individual affected by this disease has now …
average age of individual affected by of individual affected by this disease has now …
Data-based algorithms and models using diabetics real data for blood glucose and hypoglycaemia prediction–a systematic literature review
Background and aim Hypoglycaemia prediction play an important role in diabetes
management being able to reduce the number of dangerous situations. Thus, it is relevant to …
management being able to reduce the number of dangerous situations. Thus, it is relevant to …
A survey on deep learning in medicine: Why, how and when?
New technologies are transforming medicine, and this revolution starts with data. Health
data, clinical images, genome sequences, data on prescribed therapies and results …
data, clinical images, genome sequences, data on prescribed therapies and results …
Machine learning based diabetes classification and prediction for healthcare applications
The remarkable advancements in biotechnology and public healthcare infrastructures have
led to a momentous production of critical and sensitive healthcare data. By applying …
led to a momentous production of critical and sensitive healthcare data. By applying …
Forecasting of glucose levels and hypoglycemic events: head-to-head comparison of linear and nonlinear data-driven algorithms based on continuous glucose …
In type 1 diabetes management, the availability of algorithms capable of accurately
forecasting future blood glucose (BG) concentrations and hypoglycemic episodes could …
forecasting future blood glucose (BG) concentrations and hypoglycemic episodes could …
[HTML][HTML] Deep physiological model for blood glucose prediction in T1DM patients
M Munoz-Organero - Sensors, 2020 - mdpi.com
Accurate estimations for the near future levels of blood glucose are crucial for Type 1
Diabetes Mellitus (T1DM) patients in order to be able to react on time and avoid hypo and …
Diabetes Mellitus (T1DM) patients in order to be able to react on time and avoid hypo and …
[HTML][HTML] Ensemble models of cutting-edge deep neural networks for blood glucose prediction in patients with diabetes
This article proposes two ensemble neural network-based models for blood glucose
prediction at three different prediction horizons—30, 60, and 120 min—and compares their …
prediction at three different prediction horizons—30, 60, and 120 min—and compares their …
Forecasting and analyzing influenza activity in Hebei Province, China, using a CNN-LSTM hybrid model
G Li, Y Li, G Han, C Jiang, M Geng, N Guo, W Wu… - BMC Public Health, 2024 - Springer
Background Influenza, an acute infectious respiratory disease, presents a significant global
health challenge. Accurate prediction of influenza activity is crucial for reducing its impact …
health challenge. Accurate prediction of influenza activity is crucial for reducing its impact …
Enhanced blood glucose levels prediction with a smartwatch
S Pikulin, I Yehezkel, R Moskovitch - Plos one, 2024 - journals.plos.org
Ensuring stable blood glucose (BG) levels within the norm is crucial for potential long-term
health complications prevention when managing a chronic disease like Type 1 diabetes …
health complications prevention when managing a chronic disease like Type 1 diabetes …
[PDF][PDF] An Improving Long Short Term Memory-Grid Search Based Deep Learning Neural Network for Software Effort Estimation.
One of the main reasons that hinders making software effort estimation remains a most of the
unresolved problem due to the heterogeneous nature of software data with complex …
unresolved problem due to the heterogeneous nature of software data with complex …