Mining and Interpretation of Critical Aspects of Infant Health Status Using Multi-Objective Evolutionary Feature Selection Approaches
- Title
- Mining and Interpretation of Critical Aspects of Infant Health Status Using Multi-Objective Evolutionary Feature Selection Approaches
- Creator
- Piri J.; Mohapatra P.; Singh D.; Samanta D.; Singh D.; Kaur M.; Lee H.-N.
- 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. 2013 IEEE.
- Source
- IEEE Access, Vol-10, pp. 32622-32638.
- Date
- 2022-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- ant lion optimization; feature selection; genetic algorithm; Infant mortality; multi objective optimization
- Coverage
- Piri J., Department of Cse, International Institute of Information Technology, Odisha, Bhubaneswar, 751029, India; Mohapatra P., Department of Cse, International Institute of Information Technology, Odisha, Bhubaneswar, 751029, India; Singh D., Department of Computer Application, Institute of Technical Education and Research (ITER), Siksha 'o' Anusandhan (SOA) Deemed to Be University, Odisha, Bhubaneswar, 751030, India; Samanta D., Department of Computer Science, Christ University, Karnataka, Bangalore, 560029, India; Singh D., School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea; Kaur M., School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea; Lee H.-N., School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 21693536
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Piri J.; Mohapatra P.; Singh D.; Samanta D.; Singh D.; Kaur M.; Lee H.-N., “Mining and Interpretation of Critical Aspects of Infant Health Status Using Multi-Objective Evolutionary Feature Selection Approaches,” CHRIST (Deemed To Be University) Institutional Repository, accessed April 24, 2025, https://archives.christuniversity.in/items/show/15407.