Heart Disease Dataset Clusterization

Polina Dudchenko, Aleksei Dudchenko, Georgy Kopanitsa

Research output: Contribution to journalArticlepeer-review


Clusterization is a promising group of methods in the context of patient similarity. However, results of clustering are not often clear for physicians as well as different clustering methods can produce different results. We have examined a well-known dataset and implemented 3 clustering methods (k-means, Agglomerative and Spectral). We have compared and evaluated clusters and their correlation with data attributes. In contrast to original dataset's target value, the clusters correlated with only a few attributes. Finally, we train 2 predictive models based on k-nearest neighbors (KNN) algorithm and Artificial Neural Network (ANN). Models evaluation demonstrates that using the results of clustering algorithms as predictive attribute give a higher F-score than the original target attribute.

Original languageEnglish
Pages (from-to)162-167
Number of pages6
JournalStudies in Health Technology and Informatics
Publication statusPublished - 1 Jan 2019
Externally publishedYes


  • clusterization
  • patient classification
  • Patient similarity

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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