Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal International Journal of Science and Society (IJSOC)
Harizahahyu
Unknown Affiliation
Materials Science & Nanotechnology Social Sciences

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Analysis of Regression and Neural Network Models in Predicting Patient Visit Volume Harizahahyu; Friendly; Fathoni, Muhammad; Lase, Yuyun Yusnida; Prayudani, Santi; Harfita, Nur Laily
International Journal of Science and Society Vol 7 No 4 (2025): International Journal of Science and Society (IJSOC)
Publisher : GoAcademica Research & Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54783/ijsoc.v7i4.1561

Abstract

Predicting patient visit volume plays a crucial role in supporting decision-making and resource allocation in healthcare services. This study aims to compare the performance of Multiple Linear Regression and an Artificial Neural Network (ANN) in forecasting patient visits at a dental clinic, using daily patient visit data and predictor variables such as holidays and promotional activities. Multiple regression was used to capture the linear relationship between the predictor and response variables, while ANN was applied to explore potential non-linear relationships. The results indicate that multiple regression outperformed the ANN, demonstrated by lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values, and provided clearer interpretability, making it more beneficial for healthcare practitioners, particularly in the context of a limited dataset. In contrast, the ANN tended to produce overestimates and was less responsive to short-term variations. Therefore, multiple regression can still be considered a reliable, efficient, and interpretable prediction method for clinical data with a moderate sample size, while future research is recommended to use larger datasets and test other machine learning algorithms to improve the accuracy and generalizability of the results.
Co-Authors Friendly Harfita, Nur Laily Muhammad Fathoni Prayudani, Santi Yuyun Yusnida Lase