Forecasting Early-stage diabetes using Artificial Neural Network model
Keywords:
artificial neural networks, blood glucose, diabetes, relief-based filterAbstract
Diabetes is caused by high blood glucose levels, and it is a chronic disease that disrupts fat and protein metabolism. Blood glucose levels rise because it cannot be burned in the cells due to a lack of insulin secretion by the pancreas or insufficient insulin production by the cell. Diabetes risk and prevalence can be significantly reduced if accurate early detection is possible. As a result, the use of technology has become an important part of providing accurate and acceptable results in the prevention and early detection of illness. This study employs artificial neural networks to predict the early stages of diabetes by incorporating methods such as feature selection or dimension reduction through the use of a Relief-Based Filter for testing and training data. The results show 99.3% prediction accuracy and may be important in contributing to a new, highly accurate method of determining diabetes in patients.
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