Data clustering and its applications in medicine

Abstract

Artificial intelligence was first mentioned back in 1956, but the biggest leap in its use has been seen in the last two decades. It goes without saying that with the increasing availability of artificial intelligence, one of the most important areas in which it must be applied is medicine. The purpose of this short article is to provide an overview of one of the groups of machine learning, by reviewing clustering algorithms and also by reviewing the use in medicine. For an excellent interpretation, this can usually be done by finding certain groups in your data that are much easier to interpret than individual observations. This task can be solved using a clustering. In medicine, the application of clustering allows one to distinguish various groups of patients and to summarize these groups to provide much more precise recommendations. Different clustering techniques are used to identify breast cancer, Parkinson's disease, migraine, various psychological and psychiatric disorders, heart and diabetes diseases, Huntington's disease, and Alzheimer's disease, among others. Looking at all the examples, it can be seen that even when evaluating only one area, clustering is applied particularly widely. Every year, more and more different research is carried out, which allows us to apply these algorithms in medicine and helps doctors assess the situation and start treatment faster or even more accurately. It can be assumed that only a greater use of AI and clustering in medicine will be observed in the coming years.

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