GSIC: A New Interpretable System for Knowledge Exploration and Classification

Machine learning and data mining techniques have been developed rapidly in recent times. In tasks such as classification, machine learning techniques have been shown to equal to and even surpass human performance. However, high performance models are usually complex, opaque and have low interpretability thus making it difficult to explain the underlying behaviors of those models that lead to the final outcomes. In many domains such as medicine and healthcare, interpretability is one of the most important factors when considering the adoption of those models. In this paper, we propose a two-stage binary classification system applicable for healthcare (or general) data that benefits from a high level of interpretability and can at the same time achieve the results comparable to commonly used classification techniques. The motivation behind the proposed system is the lack of effective classification methods for handling data generated by various distributions (such as healthcare or banking data) that can harmonize both performance and interpretability perspectives. In this work, we tackle the problem by applying divide and conquer strategy on a new disentangled representation of the underlying data. The merit of our system is evaluated by a classification experiment with a wide range of real data and popular transparent and black-box models. Furthermore, a use case in data of sepsis patients staying in the ICU (Intensive Care Unit) is depicted to prove the interpretability of the proposed model.