# Application of Machine Learning Techniques to Predict a Patient's No-Show in the Healthcare Sector
## 4 Model Building  
In ML, classification problems are a form of supervised learning. These types of algorithms have multiple predictors and also a variable of interest responsible for guiding the analysis [39]. On the other hand, unsupervised learning has only predictors (covariates) available and focuses on identifying patterns in data sets containing data points that are neither classified nor labelled. In this study, the choice of algorithms was made based on the nature of the analyzed data and in the main goal to be achieved. As mentioned in Section 3, LR and DTs are \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Article** & **Patients** & \begin{tabular}{c} **No-Show** \\ **Rate** \\ \end{tabular} & **Service** & \
\begin{tabular}{c} **Data** \\ **Provenance** \\ \end{tabular} & **Algorithms** &
\begin{tabular}{c} **Performance** \\ **Measures** \\ \end{tabular} \\ \hline
[5] & 554,661 & Not described & Radiology & \
\begin{tabular}{c} University of Washington \\ (United States) \\ \end{tabular} & Logistic Regression &
\begin{tabular}{c} 0.770 \\ AUC ROC \\ 0.832 \\ \end{tabular} \\
[18] & Not described & 25.4\% & \begin{tabular}{c} Primary \\ Care \\ \end{tabular} & \
\begin{tabular}{c} Many Hospitals \\ (Singapore) \\ \end{tabular} & XGBoost &
\begin{tabular}{c} AUC ROC \\ 0.8962 \\ AUC ROC \\ \end{tabular} \\
[34] & 110,527 & 20.19\% & \begin{tabular}{c} Primary \\ Care \\ \end{tabular} & \
\begin{tabular}{c} Public Health \\ Service \\ (Brazil) \\ \end{tabular} & Decision Tree &
\begin{tabular}{c} 0.8962 \\ AUC ROC \\ \end{tabular} \\
[36] & 101,534 & 11.39\% & Pediatrics & \
\begin{tabular}{c} Pediatric Hospital \\ (Saudi Arabia) \\ \end{tabular} & Decision Tree &
\begin{tabular}{c} 0.8962 \\ AUC ROC \\ \end{tabular} \\
[20] & 110,527 & 20.19\% & \begin{tabular}{c} Primary \\ Care \\ \end{tabular} & \
\begin{tabular}{c} Public Health \\ (Brazil) \\ \end{tabular} & Decision Tree &
\begin{tabular}{c} 0.6 \\ AUC ROC \\ \end{tabular} \\
[35] & 110,527 & 20.19\% & \begin{tabular}{c} Primary \\ Care \\ \end{tabular} & \
\begin{tabular}{c} Public Health \\ Service \\ (Brazil) \\ \end{tabular} & Decision Tree &
\begin{tabular}{c} 0.861 \\ AUC ROC \\ \end{tabular} \\
[37] & 1,011,897 & 8.60\% & \begin{tabular}{c} Many \\ Specialities \\ \end{tabular} & \
\begin{tabular}{c} Informatics Service \\ (UK) \\ \end{tabular} & LightGBM &
\begin{tabular}{c} 0.4 to 0.61 \\ F1-score \\ \end{tabular} \\
[38] & 1,087,979 & 11.30\% & \begin{tabular}{c} Many \\ Specialities \\ \end{tabular} & Specialist Hospital & Hoeffding Trees & \begin{tabular}{c} 0.861 \\ AUC ROC \\ \end{tabular} \\ This study & 4812 & 12.52\% & \begin{tabular}{c} Motor \\ Disabilities \\ \end{tabular} & \
\begin{tabular}{c} CER Unival \\ (Brazil) \\ \end{tabular} & Logistic Regression &
\begin{tabular}{c} 0.969 \\ AUC ROC \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of studied articles. the most used algorithms to predict missing attendance, a binary classification problem. There are many other algorithms targeting classification problems, as support vector machines (SVMs) and Naive Bayes, but they are not suitable for this study based on this dataset characteristics. For example, SVMs are effective in high dimensional spaces and has as drawback not directly provide probability estimates, which implies in calculate using an expensive k-fold cross-validation. Then, the classification algorithms taken into consideration in this work are:  
* Logistic Regression classifier;
* Decision tree classifier;
* Random forest classifier. This paper made use of the open-source scikit-learn (SKLearn) ([[https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)]([https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)), accessed on 25 November 2021) library to develop supervised learning models. SKLearn library implements various ML algorithms and model performance analysis functions using the Python programming language.