# Decision Support System for Medical Diagnosis Utilizing Imbalanced Clinical Data
## 1 Introduction  
With the huge improvement in human lifestyle and the increasingly aging population, there is a growing push to develop health services at a rapid speed [1]. In China, the number of patients visiting medical health institutions reached 7.7 billion in 2015, which was 2.3% higher than the previous year [2]. Worldwide, particularly in poor countries, the shortage of medical experts is severe, forcing clinicians to serve a large number of patients during their working time [3]. Generally, clinicians distinguish patients and diagnose their diseases using their experience and knowledge; however, in doing so, it is possible for clinicians without adequate experience to commit mistakes.  
Information technology plays a vital role in changing human lifestyles. Rapid and drastic developments in the medical industry have been made utilizing information technology, and many medical systems have been produced to assist medical institutions to manage data and improve services. One survey report that medical informatics tools and machine learning techniques have beensuccessfully applied to provide recommendations for diagnosis and treatment. Therefore, automatic diagnosis is a key focus in the domain of medical informatics.  
It is common for a patient to suffer from more than one disease due to medical comorbidities. For instance, diabetes mellitus type 2 and hyperlipoidemia are likely to give rise to cardiovascular diseases [4; 5]. In fact, it has been found that a majority of patients are diagnosed as suffering from more than one disease. Automatic diagnosis suggests some possible illnesses rather than just a single illness, and the disease diagnosis problem is accordingly transferred into a multi-label learning problem. [PERSON] et al. [6] proposed a shared decision-making system for diabetes medication choice using a multi-label learning method to recommend multiple medications among eight classes of available antihyperglycemic medications. However, in this system, each label is considered independently, and label correlations are not considered. Cross-Coupling Aggregation (COCOA) [7] is a typical multi-label learning approach aimed at leveraging label correlation and exploring class imbalance. For each label, COCOA aggregates the predictive result of a binary-class learner for this label and predictive results of some multi-class learners for the pairs of this label and other labels. However, class imbalance may still affect a multi-class imbalance learner when the number of a coupling label is too small.  
To improve the performance of COCOA, a regularized ensemble approach integrated into multi-class classification process of COCOA named as COCOA-RE is presented in this paper. Considering the problem of class imbalance, this method leverages a regularized ensemble method [8] to explore disease correlations and integrates the correlations among diseases in the multi-label learning process. To provide illness diagnosis, COCOA-RE learns from the available laboratory test reports and essential information of patients and produces a multi-label predictive model. As part of this study, experiments were performed to validate the effectiveness of the proposed multi-label learning approach, and the proposed approach was implemented in a developed system prototype. The proposed system--shown in Figure 1--can help clinicians review patient conditions more comprehensively and can provide more accurate suggestions of possible diseases to clinicians.  
The rest of this paper is organized as follows: Section 2 presents the existing work about multi-label learning approaches for class-imbalanced data sets. Section 3 describes the proposed multi-label learning approach. Section 4 discusses the experimental results. Finally, Section 5 concludes our work with a summary.