# Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems
## 2 Related Work
### Supervised Techniques  
Regarding DSSs using supervised techniques, in [9], the authors presented a work that showed a compilation of machine learning algorithms used in the healthcare sector and their accuracy for different diseases. The contribution of these authors is that for a specific disease, there was a study conducted in the available literature that took the best algorithms with the top performance, and a survey which was made to allow saving research time while gathering all this information in one single paper. The best-performing algorithms for different datasets varied from Logistic Regression, Decision Tree, Support Vector Machine, Random Forest, Naive Bayes, Artificial Neural Network and K-Nearest Neighbor. Nevertheless, the work compares supervised algorithms but for different datasets which may impact the conclusions, because the best algorithm may depend on the characteristics of the dataset, the size and its features, among other particularities. The work in [6] presents a study that provides a wide overview of the relative performance of different variants of supervised machine learning algorithms for disease prediction. The authors remarked that the information of the relative performance can be used to aid researchers in the selection of an appropriate supervised machine learning algorithm for their studies. It was found that the Support Vector Machine algorithm is the most frequently applied, followed by Naive Bayes. However, the Random Forest algorithm showed a superior accuracy followed by the Support Vector Machine. The research study, in addition to comparing different supervised machine learning models, does not consider variants from each algorithm, and only a comparison between the different algorithms is made but it does not consider the hyperparameters or their tuning, which obviously has an impact on the performance results. In [7], the authors studied three supervised machine learning techniques for cancer diagnosis using the descriptions from breast masses. The work explored the use of averaging and voting ensembles to improve predictive performance. The study aimed to demonstrate that the principals can be readily applied to other complex tasks including natural language processing and image recognition. Maximum accuracy and the area under the curve (AUC) were achieved using the Support Vector Machine algorithm, wherein the prediction performance increased marginally when the algorithms were arranged into a voting ensemble. The authors used a dataset which has a low number of instances and features which show a lack of sparsity and high dimensionality, which are computationally less demanding, but in another way, could lead to overfitting and do not generalize to other test instances. The work in [10] presents a case study related to mechanical ventilation, a life-saving intervention which, when improperly delivered, can affect and injure the patient. The decision support system presented promises to reduce risks by performing the per-breath classification of five of the most widely used ventilation modes in the United States of America using the high-performance supervised machine learning model Random Forest, while having to restrict the size of the training set and maintain model generalization. The authors used almost the same size training and test datasets, which normally is not recommended for creating a machine learning model. Additionally, as the ventilation modes are so heterogeneous and can be difficult to identify, the dataset classification used for training was captured by two clinicians by mere visualization, which can be erroneous in its labeling. To conclude, the gathering of data was also confined to a single academic medical center and single ventilator type, which can be a limitation for a robust and reliable machine learning model. In [11], the authors presented a case study of people experiencing low back pain evolving into a chronic condition, unless the patient receives the right interventions at the right moment.