IMLBoost for intelligent diagnosis with imbalanced medical records

Abstract

Class imbalance of medical records is a critical challenge for disease classification in intelligent diagnosis. Existing machine learning algorithms usually assign equal weights to all classes, which may reduce classification accuracy of imbalanced records. In this paper, a new Imbalance Lessened Boosting (IMLBoost) algorithm is proposed to better classify imbalanced medical records, highlighting the contribution of samples in minor classes as well as hard and boundary samples. A tailored Cost-Fitting Loss (CFL) function is proposed to assign befitting costs to these critical samples. The first and second derivations of the CFL are then derived and embedded into the classical XGBoost framework. In addition, some feature analysis skills are utilized to further improve performance of the IMLBoost, which also can speed up the model training. Experimental results on five UCI imbalanced medical datasets have demonstrated the effectiveness of the proposed algorithm. Compared with other existing classification methods, IMLBoost has improved the classification performance in terms of F1-score, G-mean and AUC.