Journal article
Tackling missing data in community health studies using additive LS-SVM classifier
IEEE Journal of Biomedical and Health Informatics, Vol.22(2), pp.579-587
2018
Abstract
Missing data is a common issue in community health and epidemiological studies. Direct removal of samples with missing data can lead to reduced sample size and information bias, which deteriorates the significance of the results. While data imputation methods are available to deal with missing data, they are limited in performance and could introduce noises into the dataset. Instead of data imputation, a novel method based on additive least square support vector machine (LS-SVM) is proposed in this paper for predictive modeling when the input features of the model contain missing data. The method also determines simultaneously the influence of the features with missing values on the classification accuracy using the fast leave-one-out cross-validation strategy. The performance of the method is evaluated by applying it to predict the quality of life (QOL) of elderly people using health data collected in the community. The dataset involves demographics, socioeconomic status, health history, and the outcomes of health assessments of 444 community-dwelling elderly people, with 5% to 60% of data missing in some of the input features. The QOL is measured using a standard questionnaire of the World Health Organization. Results show that the proposed method outperforms four conventional methods for handling missing data-case deletion, feature deletion, mean imputation, and K-nearest neighbor imputation, with the average QOL prediction accuracy reaching 0.7418. It is potentially a promising technique for tackling missing data in community health research and other applications.
Details
- Title
- Tackling missing data in community health studies using additive LS-SVM classifier
- Authors/Creators
- G. Wang (Author/Creator)Z. Deng (Author/Creator)K-S Choi (Author/Creator)
- Publication Details
- IEEE Journal of Biomedical and Health Informatics, Vol.22(2), pp.579-587
- Publisher
- IEEE
- Identifiers
- 991005544894207891
- Copyright
- © 2016 IEEE
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Journal article
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- Domestic collaboration
- International collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.61 Artificial Intelligence & Machine Learning
- 4.61.145 Classification Algorithms
- Web Of Science research areas
- Computer Science, Information Systems
- Computer Science, Interdisciplinary Applications
- Mathematical & Computational Biology
- Medical Informatics
- ESI research areas
- Computer Science