A non-parametric predictive model for missing data: A case of Philippine public hospitals

• Main Authors: Cantor, Victor John M., Li, Richard C., Tan, Martha Lauren L., Yu, Rachelle Joy S.
• Format: text
• Published: Animo Repository 2022
• Subjects: Information resources management; Operations Research, Systems Engineering and Industrial Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/5598
• Institution: De La Salle University

Organizations have an abundance of data but actually have incomplete information. Incomplete information happens when there is missing or unreliable data which can lead to wrong decisions. A predictive model was developed using Hurwicz criterion by means of linear programming (LP). This predictive model estimates the organization's missing or unreliable data using external data from other similar organizations. The proposed predictive model was tested using data from Philippine public hospitals under the Department of Health. The model was able to provide a range of data can test the validity of incomplete information encompassing the optimistic and pessimistic decisions made by the organization.