For outsiders to higher education institutions (HEIs) in South Korea, predicting the outcomes of the International Education Quality Assurance System (IEQAS)—a Korean institutional accreditation system for HEIs—is challenging. The annual IEQAS accreditation has been conducted behind closed doors; the assessment process is confidential, and there is little access to the data for the public and individuals. However, many stakeholders, such as overseas applicants to Korean HEIs, may want to predict whether particular universities will pass the upcoming IEQAS. Hence, we sought an alternative method for the outsiders to predict a binary result of the IEQAS accreditation by utilizing disclosure data that the Korean government has published. To best predict the outcomes, we mapped out a threefold discrete model combining logistic regression, discriminant analysis, and neural network. We collected the information disclosed by the Ministry of Education in 2019 on 138 Korean private HEIs and then analyzed the secondary public dataset in line with the discrete method that ensures generalizability. Results showed (i) three education investment factors, and one school operations factor appeared as key predictors among the tested indices; (ii) education cost per student within education investment proved to be the most crucial element; and (iii) while leveraging the disclosed data turned out to be reliable, neural network’s predictive accuracy was higher than those reported using logistic regression and discriminant analysis. By processing the publicly available disclosure data, our self-study model may effectively assist in predicting IEQAS outcomes, and it can also be used as a diagnostic, prior to accreditation, by local HEIs in other nations to check their preparedness and likelihood of success within similar contexts.