Bae S, Suffoletto B, Zhang T, Chung T, Ozolcer M, Islam M, Dey A
Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study
JMIR Form Res 2023;7:e39862
URL: https://formative.jmir.org/2023/1/e39862
DOI: 10.2196/39862

This repository host the codebase and dataset used in the analysis of our work Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study with different windows and different distances from the drinking event. We evaluated the feasibility of using machine learning models to predict same-day BDEs (versus low-risk drinking events and non-drinking periods) using smartphone sensor data (e.g., accelerometer, GPS). Different algorithms (e.g., XGBoost, decision tree) were tested to assess the prediction accuracy across various “prediction distance” time windows (1-6 hours from drinking onset). Using a publicly available dataset, the best model was found to be a XGBoost model for both the weekend and weekday, with a prediction distance of 6 hours. Explainable AI (XAI) was used to explore interactions between the most informative phone sensor features contributing to BDEs.

SHAP Interaction between time of day and radius of gyration and average latitude features, respectively on weekdays (left) and on weekends (right) affecting the BDE prediction, using the test data set

PDP Contour plot of the interaction between a 15-minute average latitude and longitude predicting BDEs on weekdays (left) and weekends (right) using the test data set

Our strategy for drinking prediction modeling is as follows: using behavioral features from sensor data over a given analysis window of w hours (where we varied w), we predicted whether the subject will have a binge drinking event within d hours (with varying d to optimize the model). We use behavioral features from sensor data over a given analysis window of w hours, we predict whether the subject will have a binge drinking event within d hours . To build this predictive model, we first created a dataset containing the sensor data for each person on each day of the study, calculating features in 15-minute windows. In the dataset folder, we have created 15 datasets by combining different windows and distances. The corresponding windows and diatances according to the name of the dataset folder, such as the '15_result_indexed_dataset_window1_distance1.csv' file, window1 means its window is 1 and distance1 means its distance is 1. In total we have phone sensor data from 75 young adults.

We followed robust feature exctraction method decribed in Extraction of Behavioral Features from Smartphone and Wearable Data. Additionaly we extarct Latitude and Longitude.

Detecting Drinking Episodes in Young Adults Using Smartphone-based Sensors

Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: Implications for just-in-time adaptive interventions

Here is an example of using anaconda or miniconda for environment setup:

conda create -n bde python=3.7.16
conda activate bde
pip install -r requirements.txt

pip uninstall matplotlib
pip install matplotlib

If pdpbox install unsuccessful. Please through git (latest develop version):

$ git clone https://github.com/SauceCat/PDPbox.git
$ cd PDPbox
$ python setup.py install

Change the directory of the dataset file in the code, or put the dataset file in the same directory as the code file.

# Run this command in the project directory
conda activate bde
python code/Weekday_20%_optuna.py
python code/Weekend_20%_optuna.py

For the analysis of the data, the code can be understood as three parts

1. Data loading
2. Using optuna to get the best model parameters
3. Data analysis

Depending on the name of the file, the required dataset is called. In the sample code we use a relative path, placing the dataset and the code in a folder. After processing the dataset, the data is split using train_test_split from the sklearn package. 20% of the data is used as the final test set and 80% of the data is used as the training set.

X_train_general, X_test_general, y_train_general, y_test_general= train_test_split(x_general_1, y_general,test_size=0.2)

The training data set is divided into ten parts using cross-validation, and ten calculations are performed to find the average of the accuracies.

for train_index, test_index in kfold.split(x_general_gk):
  sub_x_train_general, sub_x_test_general = x_general_gk[train_index], x_general_gk[test_index]
  sub_y_train_general, sub_y_test_general = y_general_gk[train_index], y_general_gk[test_index]

Since each class has a different amount of data, we use smote to average the data

sub_x_train_general, sub_y_train_general = sm.fit_resample(sub_x_train_general, sub_y_train_general)

The parameters of the model are tuned using optuna, and the cross-validation process is repeated with different models to derive good model parameters.

optunan website: https://optuna.org/

Using the best model parameters obtained in the second step, a new model is generated, which is used to learn the training set and subsequently validated using the test set to obtain the final results

model = XGBClassifier(**params)

When we get the model, we use XAI to analysis it. For XAI analysis, we use two packages: pdpbox and shap.

For details see: https://github.com/slundberg/shap and https://github.com/SauceCat/PDPbox

We mainly use pdpbox to analyze PDP Contour, whose main code is shown below:

pdp_dist = pdp.pdp_isolate(model=model, dataset=X_test_general, model_features=X_test_general.columns, feature='radius_of_gyration', num_grid_points=11)

For SHAP, we used its analysis of the feature importance and Beeswarm Summary of the model. For quicker results, you can select a smaller subsection of the dataset to plot (i.e. X_test_general[:100]).

The Beeswarm Summary code is:

print(shap.summary_plot(shap_values[2], X_test_general, max_display=20))

The code for Feature Importance is:

print(shap.summary_plot(shap_values[2], X_test_general, max_display=20, plot_type='bar'))

Sangwon Bae, Tammy Chung, Denzil Ferreira, Anind K. Dey, and Brian Suffoletto. "Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: Implications for just-in-time adaptive interventions." Addictive behaviors 83 (2018): 42-47. DOI: https://doi.org/10.1016/j.addbeh.2017.11.039

Sangwon Bae, Denzil Ferreira, Brian Suffoletto, Juan C. Puyana, Ryan Kurtz, Tammy Chung, and Anind K. Dey. 2017. Detecting Drinking Episodes in Young Adults Using Smartphone-based Sensors. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 2, Article 5 (June 2017), 36 pages. DOI: https://doi.org/10.1145/3090051