# Decision Support System for Medical Diagnosis Utilizing Imbalanced Clinical Data
## 2 Related Work  
Clinical decision support systems--of which diagnosis decision support system is a representative example--are developed to assist clinicians in making accurate clinical decision using informatics tools and machine leaning techniques [9]. Boosting approaches [10], support vector machines (SVMs) [11], deep learning [12] and rule-based methods [13] have been applied in clinical decision support systems for detecting specific diseases. However, multi-label learning approaches are rarely applied in  
Figure 1: Overview of the decision support system for medical diagnosis. clinical decision support systems. One example where this type of learning approach was used was in [PERSON] et al. [6]. Using electronic health record data and applying the multi-label learning approach, the authors of that paper developed a shared decision-making system for recommending diabetes medication. According to the order of label correlation considered by the multi-label learning methods, existing approaches are divided into three categories--first-order strategy, second-order strategy, and high-order strategy. First-order strategy considers each label independently and does not take into account correlations among labels. Binary relevance (BR) [14]--a popular approach in most advanced multi-label learning algorithms--constructs an independent binary classifier for each label to achieve multi-label learning. It is easy to apply BR, but the performance of BR cannot be improved by considering correlations among labels. Multi-label learning K-nearest neighbor (ML-KNN) [15], which maximizes posterior probability to predict the labels of target examples, is a simple and effective approach for multi-label learning. Multi-Label Decision Tree (ML-DT) [16] adapts decision tree methods and produces the tree using information gained according to multi-label entropy in multi-label learning. Second-order strategy, e.g., Collective Multi-Label Classifier (CML) [17], Ranking Support Vector Machine (Rank-SVM) [18], and Calibrated Label Ranking(CLR) [19], considers correlations between a pair of labels in the learning process. For multi-label data with m labels, CLR makes m(m\(-\)1) binary classifiers, one of which is for a pair of labels. Rank-SVM produces a group of linear classifiers in the multi-label scenarios using the maximum margin principle to minimize the empirical ranking loss. To train multi-label data, CML applies maximum entropy principle to make the resulting distribution satisfy a constrain of correlations among labels. High-order strategy considers correlations among all class labels or subsets of class labels. RAndom k-labELsets (RAKEL) [20] transfers the multi-label learning task into an ensemble multi-class learning task in which each multi-class learner only handles a subset of randomly selected k labels. Some examples are normally associated with more than one label in many multi-label learning tasks. However, the number of negative examples is much larger than that of positive examples in some labels, which brings about the problem of class imbalance in multi-label learning. Class imbalance is a well-known threat in traditional classification methods [21; 22; 23]; however, it has not been extensively studied in the multi-label learning context. The existing methods towards class imbalance can be grouped into two categories. In the first case, multi-label learning methods transfer the class-imbalanced distribution into class-balanced distribution using data resampling, creating (over-sampling), or removing (under-sampling) data examples. For example, a multi-label synthetic minority over-sampling technique (MLSMOTE) [24] has been developed to produce synthetic examples associated to minority labels for imbalanced multi-label data. In this approach, the features of new examples are generated by interpolations of values belonging to the nearest neighbors.