Group Classification for the Search and Identification of Related Patterns Using a Variety of Multivariate Techniques
Metadata
Show full item recordEditorial
MDPI
Materia
Classification Cluster analysis Principal component analysis
Date
2024-03-09Referencia bibliográfica
Boukichou-Abdelkader, N.; Montero-Alonso,M.Á.;Muñoz-García, A. Group Classification for the Search and Identification of Related Patterns Using a Variety of Multivariate Techniques. Computation 2024, 12, 55. https://doi.org/10.3390/ computation12030055
Abstract
Recently, many methods and algorithms have been developed that can be quickly adapted
to different situations within a population of interest, especially in the health sector. Success has
been achieved by generating better models and higher-quality results to facilitate decision making,
as well as to propose new diagnostic procedures and treatments adapted to each patient. These
models can also improve people’s quality of life, dissuade bad health habits, reinforce good habits,
and modify the pre-existing ones. In this sense, the objective of this study was to apply supervised
and unsupervised classification techniques, where the clustering algorithm was the key factor for
grouping. This led to the development of three optimal groups of clinical pattern based on their
characteristics. The supervised classification methods used in this study were Correspondence (CA)
and Decision Trees (DT), which served as visual aids to identify the possible groups. At the same time,
they were used as exploratory mechanisms to confirm the results for the existing information, which
enhanced the value of the final results. In conclusion, this multi-technique approach was found to be
a feasible method that can be used in different situations when there are sufficient data. It was thus
necessary to reduce the dimensional space, provide missing values for high-quality information, and
apply classification models to search for patterns in the clinical profiles, with a view to grouping the
patients efficiently and accurately so that the clinical results can be applied in other research studies.