Yildirim,P.Ceken,K.2024-05-252024-05-2520200978-172818541-510.1109/UYMS50627.2020.92470522-s2.0-85097525852https://doi.org/10.1109/UYMS50627.2020.9247052https://hdl.handle.net/20.500.14517/2471In this paper we report on a study for discovering hidden patterns in commonly seen parasites by using abstracts from MEDLINE database. Parasites affect millions of people in the world and cause tremendous morbidity and mortality. Diagnosing parasites can be difficult because some symptoms and related to gene-proteins can be common to some of them. We utilize a web based biomedical text mining tool to find symptoms and gene-proteins. After selecting the most common symptoms and gene-proteins, we create two datasets with the frequencies of symptoms and gene-proteins for each parasite. For this work we selected the k-means algorithm for clustering analysis and apply it on the datasets. In addition, we compared different algorithms to observe the performance of k-means. Clustering analysis generated different types of groups of parasites. Although the results are not 100% certain, they can make positive contributions to medical researchers and experts for the diagnosis of parasites. © 2020 IEEE.eninfo:eu-repo/semantics/closedAccessbiomedical text miningclustering analysisk-means algorithmparasitesConference Object