Medical Appointment No-Show Prediction Using Machine Learning Techniques - AĞAOĞLU, MUSTAFA (My University, 2022)
© 2022 IEEE.Health care resources are limited and the efficient utilization of these resources is a must. No show for medical appointments is an everlasting problem that faces health care systems and is a huge waste of resources that can be a hindrance against improving health care services all over the world. This research aims to analyze the factors affecting this problem and create effective predictive models that can help solve it and reduce its social and economical ramifications on health care systems. This study suggests a methodology based on machine learning where different algorithms were utilized and compared and proposes a framework that can be utilized to achieve the best, and most robust classifier in terms of different performance metrics. The objective is to attempt to predict if a patient will attend his/her medical appointment or not.
Keywords: Tıp; Bilgisayar Bilimleri; Algoritmalar; Sağlık Bilimleri; Temel Tıp Bilimleri; Biyoistatistik ve Tıp Bilişimi; Mühendislik ve Teknoloji; Medicine; Computer Sciences; algorithms; Health Sciences; Fundamental Medical Sciences; Biostatistics and Medical Informatics; Engineering and Technology; Klinik Tıp (MED); Mühendislik, Bilişim ve Teknoloji (ENG); Klinik Tıp; Bilgisayar Bilimi; TIBBİ BİLİŞİM; BİLGİSAYAR BİLİMİ, YAPAY ZEKA; Clinical Medicine (MED); Engineering, Computing & Technology (ENG); CLINICAL MEDICINE; COMPUTER SCIENCE; MEDICAL INFORMATICS; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE; Yapay Zeka; Fizik Bilimleri; Bilgisayar Bilimi Uygulamaları; Bilgisayarla Görme ve Örüntü Tanıma; Tıbbi Bilişim; Artificial Intelligence; Physical Sciences; Computer Science Applications; Computer Vision and Pattern Recognition; Health Informatics; classification; data imbalance; health care; machine learning