Machine Learning for Prediction of Clinical Appointment No-Shows

Abstract

A no-show occurs when patient misses his appointment for visiting doctor in an outpatient clinic. No-shows result in inefficiencies in scheduling, capacity wastage and discontinuity in care. The study aims to develop and compare different models for predicting appointment no-shows in a hospital. The no-show estimation was made using five algorithms including Logistic Regression, Decision Tree Classifier, Random Forest, Linear Support Vector Machine and Gradient Boosting. The performance of each model is measured in terms of accuracy, specificity, precision, recall and F measure. The receiver operating characteristic curve and the precision-recall curve are obtained as further performance indicators. The result shows gradient boosting is more evident in giving consistent performance. The categorical variables used for prediction are gender, mapped age, appointment type, previous no-shows, number of previous no-shows, appointment weekday, waiting interval days, scholarship, hypertension, diabetes, alcoholism, handicap and SMS received.

The Prediction of Pediatric Outpatient No-Show Visits by Employing Machine Learning Framework

“…Historical collected data in the EHR system can be utilized to forecast future patient visits. Therefore, an intelligent digital health solution can allow healthcare facilities to strategically create and manage a long-term projection plan for their medical resources [10][11][12][13][14][15][16]. This research study aims to develop an intelligent datadriven approach based on a machine learning technique to learn from more than three million extracted pediatric medical records from MNGH database systems to predict outpatient no-show medical appointments smartly.…”