Additional file 1 of DBCSMOTE: a clustering-based oversampling technique for data-imbalanced warfarin dose prediction

Additional file 1. DBCSMOTE.zip, code files for generating minority and majority clusters in Matlab. DBCSMOTE_demo.m: the demo of DBCSMOTE together with random forest, which gives the estimated dosage. ‘num’ indicates the number of iterations of running DBCSMOTE. In each iteration, ‘evaluatePop’ calls the function to evaluate the oversampling quality. ‘train.txt’, ‘validate.txt’ and ‘test.txt’ are sub sets used for training, validation and testing. DBSCAN_fun.m: the function of algorithm DBSCAN. It conducts the clustering with two parameters (Eps and MinPts) on an input dataset and returns the samples of minority clusters and the number of clusters. RandomForest.m: the function of random forest. Random forest is an ensemble model of CARTs, which are the weak regression models. They are built on the extended training set, which is extended by DBCSMOTE. CARTprediction.m: the function of CART algorithm. This is a weak regression model of random forest. Meanwhile, this is the tool for evaluating the oversampling quality, which is generated by DBCSMOTE.