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This ML algorithm works under supervision, i.e., machine is trained with data which is well labeled and helps the model to predict with the help of dataset. Furthermore, supervised learning is divided into classification and regression. When the resultant variable is categorical, i.e., with two or more classes (yes/no, true/false, disease/no disease), we make use of classification. Whereas, when the resultant variable is a real and uninterrupted value, the problem is regression, here, a change in one variable is linked with a change in other variable (e.g., weight based on height). Some common examples of supervised ML in medicine is to perform pattern recognition over selected set of diagnosis by a cardiologist by interpretation of EKG and also from a chest X ray detection of lung module can be determined [5]. In medicine, for detection of risk in coronary heart disease, the best possible method adopted for analysis is Framingham Risk Score which is an example of supervised ML [6]. Risk models like above in medicine can guide in antithrombotic therapy in atrial fibrillation [7] and in hypertrophic cardiomyopathy for the implantation of electronic defibrillators [8].

Figure 1.3 The types of machine learning algorithm.