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Content Provider | Directory of Open Access Journals (DOAJ) |
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Author | Jayashree Piri Puspanjali Mohapatra Debabrata Singh Debabrata Samanta Dilbag Singh Manjit Kaur Heung-No Lee |
Abstract | 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. |
e-ISSN | 21693536 |
DOI | 10.1109/ACCESS.2022.3161154 |
Journal | IEEE Access |
Volume Number | 10 |
Language | English |
Publisher | IEEE |
Publisher Date | 2022-01-01 |
Publisher Place | United States |
Access Restriction | Open |
Subject Keyword | Electrical Engineering. Electronics. Nuclear Engineering Infant Mortality Feature Selection Multi Objective Optimization Genetic Algorithm Ant Lion Optimization |
Content Type | Text |
Resource Type | Article |
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