Group Classification for the Search and Identification of Related Patterns Using a Variety of Multivariate Techniques
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 t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Computation |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-3197/12/3/55 |
| _version_ | 1797241599840747520 |
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| author | Nisa Boukichou-Abdelkader Miguel Ángel Montero-Alonso Alberto Muñoz-García |
| author_facet | Nisa Boukichou-Abdelkader Miguel Ángel Montero-Alonso Alberto Muñoz-García |
| author_sort | Nisa Boukichou-Abdelkader |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T18:25:54Z |
| format | Article |
| id | doaj.art-a75e0254eee0407782faa7cf33c7e644 |
| institution | Directory Open Access Journal |
| issn | 2079-3197 |
| language | English |
| last_indexed | 2024-04-24T18:25:54Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj.art-a75e0254eee0407782faa7cf33c7e6442024-03-27T13:31:56ZengMDPI AGComputation2079-31972024-03-011235510.3390/computation12030055Group Classification for the Search and Identification of Related Patterns Using a Variety of Multivariate TechniquesNisa Boukichou-Abdelkader0Miguel Ángel Montero-Alonso1Alberto Muñoz-García2School of Doctorate in Sciences, Technologies and Engineering, University of Granada, 18012 Granada, SpainDepartment of Statistic and Operational Research, University of Granada, 18016 Granada, SpainDepartment of Statistic, University Carlos III of Madrid, 28903 Madrid, SpainRecently, 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.https://www.mdpi.com/2079-3197/12/3/55classificationclusterPCAcorrespondencesdecision treesCOPD |
| spellingShingle | Nisa Boukichou-Abdelkader Miguel Ángel Montero-Alonso Alberto Muñoz-García Group Classification for the Search and Identification of Related Patterns Using a Variety of Multivariate Techniques Computation classification cluster PCA correspondences decision trees COPD |
| title | Group Classification for the Search and Identification of Related Patterns Using a Variety of Multivariate Techniques |
| title_full | Group Classification for the Search and Identification of Related Patterns Using a Variety of Multivariate Techniques |
| title_fullStr | Group Classification for the Search and Identification of Related Patterns Using a Variety of Multivariate Techniques |
| title_full_unstemmed | Group Classification for the Search and Identification of Related Patterns Using a Variety of Multivariate Techniques |
| title_short | Group Classification for the Search and Identification of Related Patterns Using a Variety of Multivariate Techniques |
| title_sort | group classification for the search and identification of related patterns using a variety of multivariate techniques |
| topic | classification cluster PCA correspondences decision trees COPD |
| url | https://www.mdpi.com/2079-3197/12/3/55 |
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