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Time Series Analysis based Machine Learning Classification for Blood Sugar Levels

Diabetes is a chronic disease that requires lifelong treatment to keep blood sugar at a normal level. Hyperglycemia (high blood sugar) and hypoglycemia (low blood sugar) are critical blood glucose levels that should be monitored during the treatment. Alerting the patient when the blood glucose is at critical levels may minimize possible complications that may occur. Therefore, it was aimed to classify critical blood glucose levels with machine learning algorithms in this study. The performance of the classifiers has been tested with synthetic and real data. Synthetic data were created by adding noise to the sinusoidal wave while real data were obtained from diabetic patients. Features were extracted using the time series analysis method as the data is time-dependent. Machine learning algorithms were trained with these extracted features and blood glucose was classified in 5 levels (hypoglycemia, pre-hypoglycemia, normal, pre-hyperglycemia and hyperglycemia) with 95.12% accuracy.