# Application of Machine Learning Techniques to Predict a Patient's No-Show in the Healthcare Sector
## 3 Related Work  
Logistic regression (LR) and Decision Trees (DTs) are the most commonly used techniques to predict missing attendance. Based on the literature review performed by [31], [PERSON] et al. [32] was the first author to use multiple LR to identify no-shows in 1978. They used ten predictors obtained from a small sample of 291 family practice centre patients but had disappointing results. They were only able to achieve an accuracy of 67.4% in a sample with an attendance rate of 73% [31]. In contrast, the first work using DTs to identify  
Figure 11: Analysis by appointment shift. Figure 12: Analysis by appointment time. no-shows was [PERSON] and [PERSON] [33] in 1981. The study analyzed whether the use of individual characteristics of the patient was better than considering the average rate of the clinic to predict missing appointments. Many researchers have been exploring different techniques to improve the accuracy of predictive models in medical no-show contexts. The results confirm that the impact of understanding the patient's behaviour related to his absence in a medical appointment has positive effects. Another aspect to note is that depending on the medical speciality, patients have different behaviours and should be accounted for when developing predictive models. The authors of [5] presented one approach focused on features specific to the radiology environment. Data from the radiology information system (RIS) was fused with patient income estimated from the United States census data. Logistic regression models were developed to predict no-show risk among scheduled radiology appointments in a hospital setting. After the validation process, the no-show prediction model yielded an AUC of 0.770. The cited work could strengthen radiology scheduling systems that do not have capability to predict no-show presence but have as main drawbacks its computational cost of train a predictive model in a large dataset and not validate the predictive model in other similar contexts. In [18], is presented a feature engineering approach to predict a patient's risk of clinic no-show. The authors applied text mining techniques in order to extract useful information from the records collected. They build a no-show XGBoost model with the 15 top features and achieve an AUC of 0.793. Moreover, the authors describe the insights gained, and discuss modelling considerations on the trade-offs between model accuracy and complexity of deployment. Although the model developed in this work had good ability to identify clinic no-shows, it was based on a premise enterprise data warehouse that could be hardly adapted to other environments. In the other three studies, the authors used the same public dataset to build their predictive models. The public dataset refers to medical appointments scheduled in public hospitals in the city of Vitoria, in the state of Espirito Santo, Brazil. The authors of [34] started from data cleaning and processing, exploratory analysis and finally had the highest accuracy on the test set with the Decision Tree algorithm. Similarly, in [20] the authors explore the dataset following the same machine learning steps and obtained the same accuracy for both Decision Tree and Random Forest algorithms, 0.6 AUC ROC. Lastly, the work [35] also built a solution based on the Decision Tree algorithm. However, a scheduling system was implemented such that the overall model detects whether a patient has a risk of missing an appointment with a 95% accuracy, upon which it automatically enables the risky patient's schedule slot for overbooking and notifies medical staff or administration to contact them accordingly [35]. Despite all these studies being based on a dataset from the public health system in Brazil, there is a lack of information in how this dataset was collected, pre-processed and even if its under any ethical and data confidentiality protocol. Authors in [36] develop a machine learning model for predicting no-shows in paediatric outpatient clinics.