Multi-label classification for intelligent health risk prediction

Abstract

A Multi-Label Problem Transformation Joint Classification (MLPTJC) method is developed to solve the multi-label classification problem for the health and disease risk prediction based on physical examination records. We adopt a multi-class classification problem transformation method to transform the multi-label classification problem to a multi-class classification problem. Then We propose a Joint Decomposition Subset Classifier method to reduce the infrequent label sets to deal with the imbalance learning problem. Based on MLPTJC, existing cost-sensitive multi-class classification algorithms can be used to train the prediction models. We conduct some experiments to evaluate the performance of the MLPTJC method. The Support Vector Machine (SVM) and Random Forest (RF) algorithms are used for multi-class classification learning. We use the 10-fold cross-validation and metrics such as Average Accuracy, Precision, Recall and F-measure to evaluate the performance. The real physical examination records were employed, which include 62 examination items and 110, 300 anonymous patients. 8 types of diseases were predicted. The experimental results show that the MLPTJC method has better performance in terms of accuracy.