Abstract
Artificial intelligence had the most significant leap in the last two decades. In health care, artificial intelligence can be applied to many different task solutions. One of the machine learning types is unsupervised learning, and the most known type of this is clustering. Scientific researches show that clustering algorithms can be applied to identify different diseases. However, although there are many new clustering algorithms, k-means, hierarchal agglomerative clustering, and k-modes methods are still the most widely used algorithms, as these are fast-acting and work well with specific datasets. This work aims to give a brief overview of machine learning and pay more attention to unsupervised machine learning and clustering. Briefly introduce the current clustering methods in the medical field and apply the clustering methods to different medical and non-medical datasets. Results showed that different methods work best for the different datasets, and there are no universal clustering methods for all datasets. Results showed that for the E. coli dataset, the best method tested was BIRCH, but for the cancer clustering, the dataset's best model was Gaussian mixture model.
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Lukauskas, M., Ruzgas, T. (2023). Review and Comparative Analysis of Unsupervised Machine Learning Application in Health Care. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Izonin, I. (eds) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-6004-8_56
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DOI: https://doi.org/10.1007/978-981-19-6004-8_56
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