**2. Overview of Machine Learning Algorithms**

Machine learning (ML) has grown rapidly over the past few decades and has widely used applications not only limited to healthcare problems, such as predicting drug discoveries and diagnosing diseases, but also in other fields, such as mechanics, robotics, and image recognition [30–34]. In simple words, ML is a rapidly developing field of computational algorithms that aims to replicate human intelligence by adapting to their surroundings and learning from them [35]. There are two main types of machine learning algorithms: supervised and unsupervised learning [36]. The difference between these two main classes is the existence of labels in the training data subset, which will be discussed in the following sections.

## *2.1. Supervised Machine Learning*

Supervised algorithms are a subset of machine learning models which generate a function that maps inputs to desired outputs [37]. Supervised learning is characterized by the usage of labeled datasets to train algorithms for accurate classification or outcome prediction. The model adjusts its weights as input data is fed into it, achieving proper fitting during the cross-validation process [38]. During the model training process, the predicted output is compared to the actual output, and modifications are made to decrease the overall error between the two. Supervised machine learning algorithms have a broad range of applications in biosensors and healthcare, including tasks such as distinguishing cancer from non-cancer cells, detecting circulating tumor cells (CTCs), and predicting DNA quantities [31,38,39]. In the following sections, the most well-known and commonly supervised algorithms will be discussed.
