Binary Classification on French Hospital Data: Benchmark of 7 Machine Learning Algorithms

摘要

Data has become highly valuable for many of companies and organizations. With the development of advanced data science methods and computer power, extraction of intelligible knowledge using predictive models has become helpful in decision-making. In healthcare, opportunities are numerous and Machine Learning applications may help to better understand the care pathway of each patient, medical decisions, or the impact of new drugs. This article presents a benchmark of 7 Machine Learning algorithms used on binary classification tasks and applied on hospital data. The 7 algorithms were tested on 3 data sets extracted from the French national hospital database. Efficient Global Optimization algorithm was applied to avoid the bias of subjective hyperparameter tuning. ML models were compared using a K cross-validation score and ROC curves. Results show that Random Forest, combined with EGO for hyperparameter tuning, led to the best results on the 3 data sets for binary classification.