# Application of Machine Learning Techniques to Predict a Patient's No-Show in the Healthcare Sector
###### Abstract  
The health sector faces a series of problems generated by patients who miss their scheduled appointments. The main challenge to this problem is to understand the patient's profile and predict potential absences. The goal of this work is to explore the main causes that contribute to a patient's no-show and develop a prediction model able to identify whether the patient will attend their scheduled appointment or not. The study was based on data from clinics that serve the Unified Health System (SUS) at the University of Vale do Itajai in southern Brazil. The model obtained was tested on a real collected dataset with about 5000 samples. The best model result was performed by the Random Forest classifier. It had the best Recall Rate (0.91) and achieved an ROC curve rate of 0.969. This research was approved and authorized by the Ethics Committee of the University of Vale do Itajai, under opinion 4270,234, contemplating the General Data Protection Law. artificial intelligence; data science; healthcare applications; machine learning; patient attitudes +
Footnote †: journal: _Future Internet_  
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reached 30% of the total appointments in 2014-2015. According to the City of Vitoria, the average cost of an appointment during these years was approximately USD 37, which represented a loss of approximately USD 17.5 million for the government's coffers [3]. Given the impact that the non-use of health services causes in society, there is room for studies that present efficient solutions to this problem. There is a general consensus in the literature that patient nonattendance is not random, and several studies have recognized the need to statistically analyse the factors that influence a patients' no-show. Reducing the impact of missed appointments and improving health care operations are some benefits that this analysis may support. Some of the recent studies show that there is a relationship between the number of missed appointments and patients' behaviour [1; 4]. Furthermore, the work presented in [5] carried out a study in the field of hospital radiology and grouped the extracted data into three groups: patient, examination and scheduling. The most informative aspects for predicting patients' nonattendance for the exam were those based on the type of exam and the scheduling attributes, such as the waiting time between the appointment and the performance of the exam. Long waiting lines, lack of resources to meet the demand and financial loss are consequences that the patient's absence from the scheduled appointment can cause. To reduce these adverse effects, health centres have implemented various strategies, including sanctions and reminders [6]. However, during the last decades, a sizeable number of medical scheduling systems have been developed to achieve better appointment allocation based on predictive models. Therefore, machine learning (ML) algorithms can serve as efficient tools to help understand the patient's behaviour concerning their presence at a medical appointment. A better understanding of the patient's absenteeism phenomenon allows for the development of solutions to mitigate the occurrence of no-shows and contribute to the management and planning of health services. According to the context in which ML algorithms are applied, different observations are made, and new solutions can be modelled to mitigate absenteeism in medical appointments and exams. Thus, data collection and the application of ML algorithms in different contexts in healthcare should be explored, providing relevant information to assist in the decision process regarding the scheduling of appointments and exams. There is currently one publicly available dataset present for patients' no-shows which most researchers have been using.