This is a project to predict glucose by learning data collected by wearable devices and food logs.
With collected data(Accelerometer, Blood volume pulse, Electrodermal activity, Temperature, Interbeat interval, Heart rate, Food Log, Interstitial glucose concentration), feature engineering is performed to utilize meaningful features for learning.
- exploring_digital_biomarkers_a_day.ipynb : Data Exploration(for a day) Phase Related Code
- feature_engineering.ipynb : Code of the Data Preprocessing & Feature Engineering step
- df_glucose_histogram.ipynb : Histograms of three glucose level classifications(PersLow / PersNorm / PersHigh)
- randomforest_regressor.ipynb : LOOCV Random Forest Regressor & Feature Importance
- LightGBM_Classifier_forPull.ipynb : Classifier Model Using LightGBM
- lightGBM_regressor.ipynb : LOOCV Regressor Model Using LightGBM
- pycaret_classifier_undersampling.ipynb : Exploring classifier model with PyCaret (Class-balanced Using Under Sampling)
- pycaret_classifier_smote.ipynb : Exploring classifier model with PyCaret (Class-balanced Using SMOTE)
- pycaret_regressor.ipynb : Exploring LOOCV regressor model with PyCaret (PCA performed)
Cho, P., Kim, J., Bent, B., & Dunn, J. (2023). BIG IDEAs Lab Glycemic Variability and Wearable Device Data (version 1.1.2). PhysioNet. https://doi.org/10.13026/zthx-5212.
Bent, B., Cho, P.J., Henriquez, M. et al. Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches. npj Digit. Med. 4, 89 (2021). https://doi.org/10.1038/s41746-021-00465-w