COVID-19 Data Analysis with a Multi-Objective Evolutionary Algorithm for Causal Association Rule Mining
Association rule mining plays a crucial role in the medical area in discovering interesting relationships among the attributes of a data set. Traditional association rule mining algorithms such as <i>Apriori</i>, FP growth, or Eclat require considerable computational resources and genera...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Mathematical and Computational Applications |
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| Online Access: | https://www.mdpi.com/2297-8747/28/1/12 |
| _version_ | 1797619448702566400 |
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| author | Santiago Sinisterra-Sierra Salvador Godoy-Calderón Miriam Pescador-Rojas |
| author_facet | Santiago Sinisterra-Sierra Salvador Godoy-Calderón Miriam Pescador-Rojas |
| author_sort | Santiago Sinisterra-Sierra |
| collection | DOAJ |
| description | Association rule mining plays a crucial role in the medical area in discovering interesting relationships among the attributes of a data set. Traditional association rule mining algorithms such as <i>Apriori</i>, FP growth, or Eclat require considerable computational resources and generate large volumes of rules. Moreover, these techniques depend on user-defined thresholds which can inadvertently cause the algorithm to omit some interesting rules. In order to solve such challenges, we propose an evolutionary multi-objective algorithm based on NSGA-II to guide the mining process in a data set composed of 15.5 million records with official data describing the COVID-19 pandemic in Mexico. We tested different scenarios optimizing classical and causal estimation measures in four waves, defined as the periods of time where the number of people with COVID-19 increased. The proposed contributions generate, recombine, and evaluate patterns, focusing on recovering promising high-quality rules with actionable cause–effect relationships among the attributes to identify which groups are more susceptible to disease or what combinations of conditions are necessary to receive certain types of medical care. |
| first_indexed | 2024-03-11T08:28:20Z |
| format | Article |
| id | doaj.art-619e067975814b978fb21653a6402c8a |
| institution | Directory Open Access Journal |
| issn | 1300-686X 2297-8747 |
| language | English |
| last_indexed | 2024-03-11T08:28:20Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematical and Computational Applications |
| spelling | doaj.art-619e067975814b978fb21653a6402c8a2023-11-16T21:58:03ZengMDPI AGMathematical and Computational Applications1300-686X2297-87472023-01-012811210.3390/mca28010012COVID-19 Data Analysis with a Multi-Objective Evolutionary Algorithm for Causal Association Rule MiningSantiago Sinisterra-Sierra0Salvador Godoy-Calderón1Miriam Pescador-Rojas2Centro de Investigación en Computación, Instituto Politécnico Nacional, Ciudad de México 07738, MexicoCentro de Investigación en Computación, Instituto Politécnico Nacional, Ciudad de México 07738, MexicoCentro de Investigación en Computación, Instituto Politécnico Nacional, Ciudad de México 07738, MexicoAssociation rule mining plays a crucial role in the medical area in discovering interesting relationships among the attributes of a data set. Traditional association rule mining algorithms such as <i>Apriori</i>, FP growth, or Eclat require considerable computational resources and generate large volumes of rules. Moreover, these techniques depend on user-defined thresholds which can inadvertently cause the algorithm to omit some interesting rules. In order to solve such challenges, we propose an evolutionary multi-objective algorithm based on NSGA-II to guide the mining process in a data set composed of 15.5 million records with official data describing the COVID-19 pandemic in Mexico. We tested different scenarios optimizing classical and causal estimation measures in four waves, defined as the periods of time where the number of people with COVID-19 increased. The proposed contributions generate, recombine, and evaluate patterns, focusing on recovering promising high-quality rules with actionable cause–effect relationships among the attributes to identify which groups are more susceptible to disease or what combinations of conditions are necessary to receive certain types of medical care.https://www.mdpi.com/2297-8747/28/1/12association rule miningcausality measuresmulti-objective evolutionary algorithmCOVID-19 data |
| spellingShingle | Santiago Sinisterra-Sierra Salvador Godoy-Calderón Miriam Pescador-Rojas COVID-19 Data Analysis with a Multi-Objective Evolutionary Algorithm for Causal Association Rule Mining Mathematical and Computational Applications association rule mining causality measures multi-objective evolutionary algorithm COVID-19 data |
| title | COVID-19 Data Analysis with a Multi-Objective Evolutionary Algorithm for Causal Association Rule Mining |
| title_full | COVID-19 Data Analysis with a Multi-Objective Evolutionary Algorithm for Causal Association Rule Mining |
| title_fullStr | COVID-19 Data Analysis with a Multi-Objective Evolutionary Algorithm for Causal Association Rule Mining |
| title_full_unstemmed | COVID-19 Data Analysis with a Multi-Objective Evolutionary Algorithm for Causal Association Rule Mining |
| title_short | COVID-19 Data Analysis with a Multi-Objective Evolutionary Algorithm for Causal Association Rule Mining |
| title_sort | covid 19 data analysis with a multi objective evolutionary algorithm for causal association rule mining |
| topic | association rule mining causality measures multi-objective evolutionary algorithm COVID-19 data |
| url | https://www.mdpi.com/2297-8747/28/1/12 |
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