# Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems
## 2 Related Work
### Supervised Techniques
The research was initiated with the design of the decision support system using supervised machine learning with three classification models: Decision Tree, Random Forest and Boosted Tree. This study showed promising results with the Boosted Tree model and Decision Tree but must still be improved with the new collection of cases, classified as self-care cases. One limitation of this study was that the cases in the training dataset were fictitious cases on lower back pain collected during a vignette study with primary healthcare professionals which can impact the performance of the model. Additionally, we considered that the test dataset was excessively small, with only 38 real-life cases. The work in [12] presented a case study for the diagnosis of periodontal disease, which is a common infectious disease in humans that may cause cardiovascular disease and complications of coronary heart disease. With the high prevalence of periodontal disease, the prevention, identification and early treatment of periodontal disease have become extremely important. This study, using the records of 300 patients, was performed using the Support Vector Machine supervised algorithm and based on different kernel functions using the cross-validation method, showing that the radial kernel function has the best performance. Other similar studies regarding the same disease were conducted. For example, one study of 150 periodontal patients showed that the Support Vector Machine and Decision Tree have higher accuracy, while the Artificial Neural Network presented the worst results. Another study of 30 patients described the use of the Artificial Neural Network with a top precision rating. One constraint of this study was that a limited dataset size was used, where more accurate results can be obtained if more data can be used. In [13], the authors presented a case study of coronavirus disease 2019 (COVID-19), the acute respiratory disease that has been classified as a pandemic by the World Health Organization. It is crucial to identify the key factors for mortality prediction to optimize the patient treatment strategy. The dataset, after preprocessing, consisted of 1766 datapoints corresponding to 370 patients suspected of having COVID-19 from a Hospital in Wuhan. The study proposed supervised machine learning methods, namely Neural Networks, Logistic Regression, XGBoost, Random Forest, Support Vector Machine and Decision Tree, based on blood tests to predict COVID-19, using a strong combination of five features. The results showed that for feature importance and classification, XGBoost and Neural Network, respectively, demonstrated the top performances. Other machine learning models involving trees and regression algorithms performed the next best performance results. The work in [14] presented a study to support clinicians and researchers in machine learning approaches in the field of infection management. Supervised machine learning techniques were used, including Logistic Regression in 18 studies, followed by Random Forest, Support Vector Machine and Artificial Neural Networks in 18, 12 and 7 studies, respectively. The best-performing techniques were Long Short-Term Memory Networks, Artificial Neural Network, Logistic Regression, Support Vector Machine, Regression Tree and Stochastic Gradient Boosting. Some limitations of this study included the fact that the comparability of the different approaches per research area was limited and should be interpreted with great caution. There is large heterogeneity between the identified studies, namely in terms of predicted outcomes, the features used and the study size. Knowing that the operations of cleaning and transforming, normally represents the majority percentage of the entire work of applying a machine learning model to data, in 39% of the previous carried studies there was no mention to this kind of operations.