## 2 Related Work on Machine Learning for Medical Systems  
Machine learning these days is one of the many technologies employed in medicine and particularly medical data analysis. To this day, there are countless approaches of machine learning that are applied in various medical problems, whether it is hospital readmission, diagnosis or treatment plans, and even newer methods and applications are being developed as of now [22; 23; 24; 25]. Machine learning is predicted to create processes performed by both humans and computers. In these instances, there is a need to achieve an optimal combination by leveraging human abilities of hypotheses generation, collaboration and AI systems oversight along with the AI abilities of analysis of large volumes of data, finding associations with predictive power, or optimization against a success criterion [26]. The basic used algorithms are Linear Regression, Logistic Regression, Decision Tree, Support Vector Machines (SVM), K-means, Random Forest (or Random Decision Forests), k-Nearest Neighbors algorithm (kNN), Dimensionality Reduction, Gradient Boosting and Extreme Gradient Boosting (XGBoost), variations of Neural Networks, Logistic Regression, and Naive Bayes Classifiers [27]. A support vector machine (SVM) is a supervised machine learning algorithm that classifies data into two categories. It uses a series of data already sorted into two classes and builds its model of it during training, drawing lines between established categories. The Naive Bayes classifier is a well-known approach in machine learning that has been applied in healthcare for some time, is a modification of the normal Bayes classifier, and uses probability to solve the task of classification. Its basic principle is building probability distributions per trait for each class. Despite the discussed benefits [28] of Fuzzy Inference Systems (FIS) and the related Adaptive-Network-based Fuzzy Inference System (ANFIS) in healthcare, in recent years, they are not as frequently mentioned in scientific literature. FIS are built upon ideas such as fuzzy sets, fuzzy control and fuzzy-logic systems and use fuzzy theory for mapping inputs to outputs. There are two known conventional types of FIS: Mamdani and Sugeno. The Mamdani fuzzy inference system was designed with the purpose of operating a steam engine and boiler combination, and it was meant to utilize sets of linguistic control rules obtained from the experienced human operators. The purpose of the Sugeno fuzzy inference system was to develop a systematic approach of fuzzy rules generation from a given input-output dataset. Despite the evident benefits of the usage of machine learning systems in the field of healthcare, the technology is still facing many problems in practice, such as data heterogeneity and a lack of good enough hardware [29; 30; 31; 32], as well as general human distrust towards machine assistance [24; 33; 34]. Data heterogeneity refers to the issue of coming from different, disparate data sources. The data collected from different resources come in a wide variety of data formats and types [35], and as a result, requires additional transformation before they can be utilized in any automation or calculations. Data heterogeneity has been a major problem in fields such as statistics, computer science, Internet of Things, and, essentially, machine learning, which includes, but is not limited to, machine learning in the context of healthcare. Another problem that is prevalent in machine learning-based health systems is the difference between various approaches in purpose, performance, and computational requirements. More computation-heavy techniques like deep learning may not be fit for organizations that do not have powerful hardware to run these programs efficiently [23; 36; 37]. Finally, there is a psychological problem that lies in a certain amount of mistrust towards machine learning in fields as crucial as medicine [38; 39; 40; 41]. People outside the technological fields show reluctance to use methods that cannot be easily explained to them.