Early Stage Diabetes Likelihood Prediction using Artificial Neural Networks

Gamara, Rex Paolo C.; Loresco, Pocholo James M.; Vicerra, Ryan Rhay P.

doi:10.1109/hnicem51456.2020.9400075

Cited by 7 publications

(1 citation statement)

References 13 publications

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“…In addition, diabetes risk classification is based on patient symptom information using ANN. The SCG backpropagation or scaled conjugate gradient technique was used for NN [17].…”

Section: Related Work In Diabetesmentioning

confidence: 99%

Comparative analysis in the prediction of early-stage diabetes using multiple machine learning techniques

Flores,

Hernandez,

Macatangay

et al. 2023

IJEECS

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Diabetes is caused by high levels from blood glucose and it is characterized as a chronic disease, and also will disrupt fat and protein absorption. The levels rise from blood glucose because it cannot be burned in the cells from the pancreas because of the deficiency of insulin secretion or the insulin produce by the cell are insufficient. By means of early detection, it may decrease the hazards and frequency of diabetes. The application of technology has been an essential part of providing accurate and acceptable results in the prevention and early detection of the illness. This research provided the best machine learning used for predicting the early stage of diabetes. The methods involve the feature selection or dimension reduction using relief-based filter (reliefF), tenfold cross-validation for testing and training data, and different machine learning classifiers such as the random forest (RF), support vector machines (SVM), and neural network (NN) is used. in this research, RF recorded the highest precision point at 98.5%, which was able to provide a higher evaluation in terms of accuracy followed by SVM at 96.6% and NN at 96.2%. The results generated from this experiment are essential in contributing a new way that is highly accurate in determining diabetes among patients.

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“…In addition, diabetes risk classification is based on patient symptom information using ANN. The SCG backpropagation or scaled conjugate gradient technique was used for NN [17].…”

Section: Related Work In Diabetesmentioning

confidence: 99%