# Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems
## 1 Introduction  
Decision support systems (DSSs) are well-established types of information systems with the primary purpose of improving decision making based on data and analysis. They analyze massive amounts of data through the comprehensive compilation of information to solve problems and support decision making. With this information, the system produces reports that may project revenue, sales, or manage inventory. These systems are very important for many different industries, from healthcare to agriculture. A medical clinician using a computerized decision support system which combines the clinician inputs and previous electronic health records can assist in diagnosing and prescribing the patient. Healthcare is one of the fastest growing sectors and is currently in the middle of a global eventual and transformation. Global healthcare costs, currently estimated between USD 6 trillion and USD 7 trillion, are projected to reach more than USD 12 trillion in 2024 [1]. Regarding this rapid growth in costs, measures need to be taken in order to ensure that healthcare costs do not further spin out of control. Machine learning has been identified as having major technological applications in the healthcare realm, where it will probably never completely replace physicians but will certainly transform the healthcare sector, benefiting both patients and providers. Machine learning is a subfield of artificial intelligence that gives computers the ability to learn. It focuses on the use of data and algorithms to imitate the way in which humans learn [2]. The process of learning starts with data observation with examples, direct experience or instruction, so it can look for patterns in the provided data to supportdecisions in the future. The goal is to allow computers to learn automatically without human intervention and automatically adjust actions accordingly [3]. Machine learning algorithms are often categorized as either supervised and unsupervised. Supervised machine learning algorithms learn from labeled examples so new data can be predicted. The learning algorithm compares the output with the correct result, finding errors in order to modify the model accordingly. Unsupervised machine learning algorithms are characterized as systems that do not know the right output but explore the data and draw inferences from datasets to describe hidden structures from unlabeled data. Clustering is considered to be the most important technique of unsupervised learning [4]. The definition of a cluster might be seen as a collection of data objects which are similar to one another within the same group and are different from the objects in other clusters. Clustering can work as a standalone tool to derive insights about the data distribution or as a preprocessing step in other algorithms. Most works in the industry apply supervised machine learning techniques as they are more prone to using such techniques and can be clearly compared with unsupervised learning whilst supervised learning provides more relevant results; hence, artificial applications in the industry most often use supervised learning [5]. A recent study [6] showed that the most frequently used algorithms for prediction were Support Vector Machine followed by Naive Bayes. However, the Random Forest algorithm presented superior accuracy. All of them are supervised machine learning techniques [6]. Another study [7], for cancer diagnosis, used supervised machine learning techniques, where the Support Vector Machine achieved maximum accuracy. Another study [8] on postpartum depression used six supervised machine learning algorithms, namely Logistic Regression, Support Vector Machine, Decision Tree, Naive Bayes, Extreme Gradient Boosting (XGBoost) and Random Forest. As a result, the Support Vector Machine model was the best-performing model.