This is a MATLAB solution to the Melbourne University AES/MathWorks/NIH Seizure Prediction (https://www.kaggle.com/c/melbourne-university-seizure-prediction).
It is built upon a winning solution (https://github.com/drewabbot/kaggle-seizure-prediction) from a previous Kaggle seizure competition (https://www.kaggle.com/c/seizure-prediction), using lassoGLM models from the Statistics and Machine Learning Toolbox.
The zip file contains:
1. calculate_features.m -- a function that calculates a set of features from the iEEG sample values
2. step1_generate features -- a function that loads in patient training (and test) iEEG sample data and calculates features (using function calculate_features.m).
3. step2_generate_models.m -- a function that trains a LASSO GLM model using the features extracted from step1_generate_features.m.
4. step3_evaluate_models.m -- a function that evaluates the trainted model and returns in-sample and out-of-sample skill for model assessment. Output is stored to a .csv file for Kaggle submission.
5. step4_predict_seizure.m -- a function that make predictions on test data, with the model trained and evaluated from the previous steps. Output is stored to a .csv file for Kaggle submission.
6. kaggle_seizure_prediction_workflow.m -- a master script that runs through the above-mentioned steps.
Jianghao Wang (2023). Kaggle: seizure prediction MATLAB solution -- LASSO GLM approach (https://www.mathworks.com/matlabcentral/fileexchange/59158-kaggle-seizure-prediction-matlab-solution-lasso-glm-approach), MATLAB Central File Exchange. Retrieved .
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