Fig. 4
From: Machine learning for precision diagnostics of autoimmunity
Machine learning methods applied to integrated data. (A) ROC curve calculations using autoimmune data revealed how different types of data impact the performance of machine learning model with a representative example of a random forest classifier. AUC increased once clinical data was integrated to additional data types such as laboratory and genomics. (B) The performance of selected classifiers was evaluated and compared using stratified cross-validations, revealing an improvement in prediction accuracy for each model when integrated data is used compared to a single data type.
