ABSTRACT
Machine Learning (ML) is extensively utilized in the health care system to analyse data from many perspectives and to assist in the extraction and summarization of important facts and statistics. The modern health care system generates an enormous data that is stored in medical databases. The manual analysis of this data is a time-consuming and tedious process. Hence, it is essential to develop a model that aids in extracting valuable data and provides systematic supervision for medical analysis. Primary disease analysis is a critical component in health care to provide appropriate patient care and treatment. Any disease diagnosis for humans should be accurate and effective as it is closely linked to humankind. Medical professionals analyse human illnesses based on their experience and data from pathology. Forecasting drastically limited data is both a difficult endeavour and an open topic. In the sphere of health care, prediction plays an essential role. The use of ML at this stage could broaden the perspective and raise the degree of decision-making. The tools and algorithms available in ML assist in the building of programmes that can aid in making quick and accurate judgements. In ML, classification algorithms use input training data to predict whether subsequent data will fall into one of the several specified classes or groups. To put it another way, classification is the task of simplifying a known structure so that it may be applied to fresh data.
The prime goal of this chapter is the evaluation of classification-based prediction approach functionality in health care systems. This will be supported by comprehensive discussions of several classification algorithm strategies in ML. It also aims to identify numerous techniques and applications that are used in classification-based prediction in the field of health care. This chapter will also go through the performance measures for classification. In addition, a detailed description of all of the classification algorithms that have been extensively used in the health care domain will be provided. The foundation will be laid by offering a comprehensive overview of context and the history of classification algorithms.
