Introduction:A prompt severity assessment model of patients with confirmed infectious diseases could enable efficient diagnosis while alleviating burden on the medical system. This study aims to develop a SARS-CoV-2 severity assessment model and establish a medical system that allows patients to check the severity of their cases and informs them to visit the appropriate clinic center on the basis of past treatment data of other patients with similar severity levels.Methods: This paper provides the development processes of a severity assessment model using machine learning techniques and its application on SARS-CoV-2-infected patients. The proposed model is trained on a nationwide data set provided by a Korean government agency and only requires patients' basic personal data, allowing them to judge the severity of their own cases. After modeling, the boosting-based decision tree model was selected as the classifier while mortality rate was interpreted as the probability score. The data set was collected from all Korean citizens with confirmed COVID-19 between February 2020 and July 2021 (N = 149,471).Min Sue Park and Hyeontae Jo contributed equally and are co-first authors.

No abstract

As the amount of data increases, it is more likely that the assumptions in the existing economic analysis model are unsatisfied or make it difficult to establish a new analysis model. Therefore, there has been increased demand for applying the machine learning methodology to bankruptcy prediction due to its high performance. By contrast, machine learning models usually operate as black-boxes but credit rating regulatory systems require the provisioning of appropriate information regarding credit rating standards. If machine learning models have sufficient interpretablility, they would have the potential to be used as effective analytical models in bankruptcy prediction. From this aspect, we study the explainability of machine learning models for bankruptcy prediction by applying the Local Interpretable Model-Agnostic Explanations (LIME) algorithm, which measures the feature importance for each data point. To compare how the feature importance measured through LIME differs from that of models themselves, we first applied this algorithm to typical tree-based models that have ability to measure the feature importance of the models themselves. We showed that the feature importance measured through LIME could be a consistent generalization of the feature importance measured by tree-based models themselves. Moreover, we study the consistency of the feature importance through the model's predicted bankruptcy probability, which suggests the possibility that observations of important features can be used as a basis for the fair treatment of loan eligibility requirements.

No abstract

No abstract