Mining and Interpretation of Critical Aspects of Infant Health Status Using Multi-Objective Evolutionary Feature Selection Approaches
The rate of infant mortality (IMR) in a population under one year of age is a marker for infant mortality. It is a major sensitive marker of a community’s overall physical health. Protecting the lives of newborns has become a challenging issue in public health, development programs, and h...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9739006/ |
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| author | Jayashree Piri Puspanjali Mohapatra Debabrata Singh Debabrata Samanta Dilbag Singh Manjit Kaur Heung-No Lee |
| author_facet | Jayashree Piri Puspanjali Mohapatra Debabrata Singh Debabrata Samanta Dilbag Singh Manjit Kaur Heung-No Lee |
| author_sort | Jayashree Piri |
| collection | DOAJ |
| description | The rate of infant mortality (IMR) in a population under one year of age is a marker for infant mortality. It is a major sensitive marker of a community’s overall physical health. Protecting the lives of newborns has become a challenging issue in public health, development programs, and humanitarian initiatives. Almost 10.1% infants died in the United States of America (USA) in 2021. Therefore, this paper aims to extract and understand the various influential factors causing infant deaths in the USA. A crowding distance-based multi-objective ant lion optimization (MOALO-CD) is proposed here with statistical evidence for feature selection. The proposed technique is compared with competitive metaheuristic models such as multi-objective genetic algorithm based on crowding distance (MOGA-CD), multi-objective filter approaches, and recursive feature elimination. Various machine learning classifiers are applied to the selected feature subset obtained from MOALO-CD on the USA’s infant dataset. Extensive experimental results indicate that the proposed model outperforms the existing metaheuristic approaches in terms of Generational Distance, Inverted Generational Distance, Spread, and Hyper volume. Also, the comparative analysis of various machine learning models reveals that random forest achieves significantly better performance on the feature subset obtained from MOALO-CD. |
| first_indexed | 2024-12-14T05:48:44Z |
| format | Article |
| id | doaj.art-e5f5479f12ca4adab254d842fed301c4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T05:48:44Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e5f5479f12ca4adab254d842fed301c42022-12-21T23:14:48ZengIEEEIEEE Access2169-35362022-01-0110326223263810.1109/ACCESS.2022.31611549739006Mining and Interpretation of Critical Aspects of Infant Health Status Using Multi-Objective Evolutionary Feature Selection ApproachesJayashree Piri0https://orcid.org/0000-0003-0360-0426Puspanjali Mohapatra1https://orcid.org/0000-0002-1718-1640Debabrata Singh2Debabrata Samanta3https://orcid.org/0000-0003-4118-2480Dilbag Singh4https://orcid.org/0000-0001-6475-4491Manjit Kaur5https://orcid.org/0000-0001-8804-9172Heung-No Lee6https://orcid.org/0000-0001-8528-5778Department of CSE, International Institute of Information Technology, Bhubaneswar, Bhubaneswar, Odisha, IndiaDepartment of CSE, International Institute of Information Technology, Bhubaneswar, Bhubaneswar, Odisha, IndiaDepartment of Computer Application, Institute of Technical Education and Research (ITER), Siksha ‘O’ Anusandhan (SOA) Deemed to be University, Bhubaneswar, Odisha, IndiaDepartment of Computer Science, CHRIST University, Bangalore, Karnataka, IndiaSchool of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South KoreaSchool of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South KoreaSchool of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South KoreaThe rate of infant mortality (IMR) in a population under one year of age is a marker for infant mortality. It is a major sensitive marker of a community’s overall physical health. Protecting the lives of newborns has become a challenging issue in public health, development programs, and humanitarian initiatives. Almost 10.1% infants died in the United States of America (USA) in 2021. Therefore, this paper aims to extract and understand the various influential factors causing infant deaths in the USA. A crowding distance-based multi-objective ant lion optimization (MOALO-CD) is proposed here with statistical evidence for feature selection. The proposed technique is compared with competitive metaheuristic models such as multi-objective genetic algorithm based on crowding distance (MOGA-CD), multi-objective filter approaches, and recursive feature elimination. Various machine learning classifiers are applied to the selected feature subset obtained from MOALO-CD on the USA’s infant dataset. Extensive experimental results indicate that the proposed model outperforms the existing metaheuristic approaches in terms of Generational Distance, Inverted Generational Distance, Spread, and Hyper volume. Also, the comparative analysis of various machine learning models reveals that random forest achieves significantly better performance on the feature subset obtained from MOALO-CD.https://ieeexplore.ieee.org/document/9739006/Infant mortalityfeature selectionmulti objective optimizationgenetic algorithmant lion optimization |
| spellingShingle | Jayashree Piri Puspanjali Mohapatra Debabrata Singh Debabrata Samanta Dilbag Singh Manjit Kaur Heung-No Lee Mining and Interpretation of Critical Aspects of Infant Health Status Using Multi-Objective Evolutionary Feature Selection Approaches IEEE Access Infant mortality feature selection multi objective optimization genetic algorithm ant lion optimization |
| title | Mining and Interpretation of Critical Aspects of Infant Health Status Using Multi-Objective Evolutionary Feature Selection Approaches |
| title_full | Mining and Interpretation of Critical Aspects of Infant Health Status Using Multi-Objective Evolutionary Feature Selection Approaches |
| title_fullStr | Mining and Interpretation of Critical Aspects of Infant Health Status Using Multi-Objective Evolutionary Feature Selection Approaches |
| title_full_unstemmed | Mining and Interpretation of Critical Aspects of Infant Health Status Using Multi-Objective Evolutionary Feature Selection Approaches |
| title_short | Mining and Interpretation of Critical Aspects of Infant Health Status Using Multi-Objective Evolutionary Feature Selection Approaches |
| title_sort | mining and interpretation of critical aspects of infant health status using multi objective evolutionary feature selection approaches |
| topic | Infant mortality feature selection multi objective optimization genetic algorithm ant lion optimization |
| url | https://ieeexplore.ieee.org/document/9739006/ |
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