Loading...
Publication
Cluster analysis for breast cancer patterns identification
| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 3.18 MB | Adobe PDF |
Advisor(s)
Abstract(s)
Safety in patient decision-making is one of the major health
care challenges. Computational support in establishing diagnoses and
preventing errors will contribute to an enhancement in doctor-patient
communication. This work performs a three-dimensional cluster analysis,
using k-means algorithm, to identify patterns in a breast cancer
database. The methodology proposed can be useful to identify patterns
in the database that are normally difficult to be noted by classical methods,
such as statistical methods. The three-dimensional cluster approach
was explored combining three variables at once. The k-means algorithm
is used to recognize the hidden patterns on the database. Sub-clusters
are used to separate the benign and malignant tumors inside the global
cluster. The results present effective analyses of three different clusters
based on different combinations between variables. Thus, health professionals
can obtain a better understanding of the properties of different
types of tumor, identifying the mined abstract tumor features, through
the cluster data analysis.
Description
Keywords
Cluster analysis Disease diagnosis Breast cancer
Pedagogical Context
Citation
Azevedo, Beatriz Flamia; Alves, Filipe; Rocha, Ana Maria A.C.; Pereira, Ana I. (2021). Cluster analysis for breast cancer patterns identification. In Pereira, Ana I.; Fernandes, Florbela P.; Coelho, João Paulo; Teixeira, João Paulo; Pacheco, Maria F.; Alves, Paulo; Lopes, Rui Pedro (Eds.) Optimization, learning algorithms and applications: first International Conference, OL2A 2021. Cham: Springer Nature. p. 508-514. ISBN 978-3-030-91884-2
Publisher
Springer Nature