Patient Visit Prediction Using the C5.0 Algorithm at Regina Maris Hospital

Muhammad Sowban Adilla; Rakhmat Kurniawan R; Raissa Amanda Putri

doi:10.55537/cosie.v5i2.1670

Authors

Muhammad Sowban Adilla Universitas Islam Negeri Sumatera Utara
Rakhmat Kurniawan R Universitas Islam Negeri Sumatera Utara
Raissa Amanda Putri Universitas Islam Negeri Sumatera Utara

DOI:

https://doi.org/10.55537/cosie.v5i2.1670

Keywords:

Hospital, Health, Data Mining, C5.0 Algorithm, Web

Abstract

The increasing number of patient visits demands hospital readiness in providing facilities and supporting infrastructure for healthcare services, especially in planning drug needs and medical services. Inappropriate planning can lead to a mismatch between the number of patients and the availability of resources. Therefore, a patient visit prediction system is needed that can assist hospitals in decision-making. This study aims to predict patient visits at Regina Maris Hospital Medan to support service planning and improve patient satisfaction. The method used is data mining by applying the C5.0 algorithm as a classification method. Patient visit data is processed to produce a prediction model in the form of a decision tree and rules. The results show that the C5.0 algorithm is able to produce an accuracy rate of 71% in predicting patient visits. Thus, the system developed can be used as a recommendation tool for hospitals in preparing service needs, especially related to drug management and other supporting facilities.

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