Abstract
Machine learning has only recently begun to see its application in medicine and is still facing quite a few challenges that prevent it from being more widely used. Problems such as high data dimensionality and the lack of a common data schema still remain relevant. It is worth examining the usage of machine learning in the context of healthcare and deploying selected machine learning algorithms on the problem of cardiovascular disease diagnosis. Cardiovascular diseases are currently the most common cause of death in the world. Many of them develop for a long time in an asymptomatic way, and when the first symptoms become visible, it is often too late to implement effective treatment. For this reason, it is important to carry out regular diagnostic tests that will allow you to detect a given disease at an early stage. It is then possible to implement appropriate treatment that will prevent the occurrence of an advanced form of the disease. While doing so, it attempts to analyse data from different sources and utilizing natural language processing to combat data heterogeneity. The paper assesses the efficiency of various approaches of machine learning (i.e., TR-SVM (Terminated Ramp–Support Vector Machine), TWNFI (Transductive Neuro-Fuzzy Inference), Naive Bayes) when applied in the healthcare field and proposes the solutions to the problem of plain text data transformation and data heterogeneity with the help of natural language processing. The algorithms used for diagnosis were implemented, tested and their performance compared, with their parameters also investigated, making it easier to choose an algorithm better suited for a specific case. Whereas TRSVM is better suited for smaller datasets with a high amount of dimensions, TWNFI performs better on larger ones and does not have the performance problems.
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