Performance evaluation of triumvirate clustering algorithms for heart disease prediction
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 11, Pages 7780-7789
AbstractNow a day’s heart disease is the dominant reason for deaths far and wide.Vast number of people annually suffers from heart malfunction worldwide. A heart patient shows several symptoms and it is very tough to attribute them to the heart disease in so many steps of disease progression. Data mining, as an answer to extract a hidden pattern from the clinical dataset, are applied to a database in this analysis. Clustering is an important means of data mining based on separating data categories by similar features. This study showed that how to obtain the clusters and how to determine the new centroid using K-means,EM and Farthest first algorithms.The k-means algorithm is one of thewidely recognized clustering tools that are applied in a varietyof scientific and industrial applications. The EM technique is similar to the K-Means technique.Instead of assigning examples to clusters to maximize the differences in means for continuous variables, the EM clustering algorithm computes probabilities of cluster memberships based on one or more probability distributions.Farthest first algorithm is suitable for the large dataset but it creates the non-uniform cluster. Finally this study examined the performance of these three algorithms.The dataset of 303 people were collected from Cleveland dataset of UCI machine learning.
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