TY  - JOUR
T1  - Predicting and Reducing Patient Waiting Times in Dental Clinics Using Machine Learning: A Case Study from Türkiye
TT  - Predicting and Reducing Patient Waiting Times in Dental Clinics Using Machine Learning: A Case Study from Türkiye
AU  - Keskin, Abdulkadir
PY  - 2025
DA  - January
Y2  - 2024
DO  - 10.34248/bsengineering.1574470
JF  - Black Sea Journal of Engineering and Science
JO  - BSJ Eng. Sci.
PB  - Karyay Karadeniz Yayımcılık Ve Organizasyon Ticaret Limited Şirketi
WT  - DergiPark
SN  - 2619-8991
SP  - 243
EP  - 248
VL  - 8
IS  - 1
LA  - en
AB  - Long waiting times in polyclinics are a critical factor affecting patient satisfaction and the efficient use of healthcare personnel and resources. This study applied machine learning (ML) algorithms to predict and reduce patient waiting times in a dental clinic in Türkiye. The daily data collected from the clinic included variables such as patient satisfaction, appointment patients, Walk-in patients, number of doctors and nurses, and dental technicians on duty. Six ML algorithms were tested: Decision Trees (DT), Linear Regression (LR), Support Vector Machines (SVM), Gaussian Process Regression (GPR), Kernel Regression (KR), and Neural Networks (NN). Among these, the GPR model achieved the best performance, accurately predicting patient waiting times with an R2 value of 0.936 and RMSE of 0.075. This study highlights the potential of ML methods to enhance operational efficiency in healthcare management.
KW  - Healthcare management
KW  - Waiting time prediction
KW  - Dental clinic
KW  - Machine learning
N2  - Long waiting times in polyclinics are a critical factor affecting patient satisfaction and the efficient use of healthcare personnel and resources. This study applied machine learning (ML) algorithms to predict and reduce patient waiting times in a dental clinic in Türkiye. The daily data collected from the clinic included variables such as patient satisfaction, appointment patients, Walk-in patients, number of doctors and nurses, and dental technicians on duty. Six ML algorithms were tested: Decision Trees (DT), Linear Regression (LR), Support Vector Machines (SVM), Gaussian Process Regression (GPR), Kernel Regression (KR), and Neural Networks (NN). Among these, the GPR model achieved the best performance, accurately predicting patient waiting times with an R2 value of 0.936 and RMSE of 0.075. This study highlights the potential of ML methods to enhance operational efficiency in healthcare management.
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UR  - https://doi.org/10.34248/bsengineering.1574470
L1  - https://dergipark.org.tr/tr/download/article-file/4318806
ER  -