Automated Machine Learning to Develop Predictive Models of Metabolic Syndrome in Patients with Periodontal Disease

Authors: Ovidiu Boitor, Florin Stoica, Romeo Mihăilă, Laura Florentina Stoica and Laura Stef

This is the code repository for the paper Automated Machine Learning to Develop Predictive Models of Metabolic Syndrome in Patients with Periodontal Disease.
We used Automated Machine Learning (AutoML) frameworks to build predictive models in order to determine which of these risk factors exhibits the most robust association with metabolic syndrome. To gain confidence in the results provided by the machine learning models provided by the AutoML pipelines, we used SHapley Additive exPlanations (SHAP) values for the interpretability of these models, from a global and local perspective. Contains practical approaches for the following AutoML frameworks:

• Auto-sklearn
• H2O AutoML

Notes on Environment Setup

• There are two CSV files with example data for training and testing.
• Software package versions: Python 3.8.X, Auto-sklearn 0.14.3, H2O cluster version 3.36.0.1