Analysis of Outpatient Patient Visit Prediction at Muntilan Regional General Hospital Using Linear Regression Method

Dwi  Susanti; Purwono Hendradi; Rofi Abul Hasani

doi:10.47709/cnahpc.v7i1.5271

Authors

Dwi Susanti Computer Science Department, Universitas Muhammadiyah Magelang, Indonesia
Purwono Hendradi Computer Science Department, Universitas Muhammadiyah Magelang, Indonesia
Rofi Abul Hasani Computer Science Department, Universitas Muhammadiyah Magelang, Indonesia

DOI:

https://doi.org/10.47709/cnahpc.v7i1.5271

Keywords:

Data Analysis, Healthcare Planning, Linear Regression, Predictive Modeling, Resource Management

Abstract

Hospitals play a crucial role in public health, and understanding patient visit patterns is essential for effective service delivery. Thus, accurate predictions are vital for resource planning, service improvement, and addressing challenges like long wait times and overcrowding. This study focuses on predicting outpatient visits at RSUD Muntilan, a regional general hospital in Magelang, Indonesia. The method used was the linear regression method. The research involved data collection from the hospital's information system, pre-processing to prepare the data, dataset formation, model creation using linear regression, and model evaluation. The study utilized historical outpatient visit data FROM 2021 TO 2024 to develop a linear regression model that predicts the number of visits for the next three months. The model's performance was evaluated using the Mean Absolute Percentage Error (MAPE), which yielded a value of 15.33%. This indicates that the model's predictions were, on average, within 15.33% of the actual values, demonstrating an accuracy of 84.67%. The successful application of the linear regression method in this study highlights its potential for improving resource allocation, enhancing service efficiency, and ultimately enhancing the overall quality of healthcare services provided by RSUD Muntilan. The findings emphasize the significance of data-driven approaches and predictive analytics in optimizing healthcare operations and meeting the evolving needs of the community.

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