ArticleInternational journal of endocrinology2013
Comparison of classification algorithms with wrapper-based feature selection for predicting osteoporosis outcome based on genetic factors in a taiwanese women population.
Article in International journal of endocrinology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 24 citations in OpenAlex.
- An Enhanced Ant Colony Optimization Mechanism for the Classification of Depressive Disorders.Computational intelligence and neuroscience · 2022Article
- Classification of osteoporosis by artificial neural network based on monarch butterfly optimisation algorithm.Healthcare technology letters · 2018Article
- Artificial intelligence on the identification of risk groups for osteoporosis, a general review.Biomedical engineering online · 2018Review
- NMFBFS: A NMF-Based Feature Selection Method in Identifying Pivotal Clinical Symptoms of Hepatocellular Carcinoma.Computational and mathematical methods in medicine · 2015Article
- Identifying potential clinical syndromes of hepatocellular carcinoma using PSO-based hierarchical feature selection algorithm.BioMed research international · 2014Article
- Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches.Dento maxillo facial radiologyArticle
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
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Abstract
An essential task in a genomic analysis of a human disease is limiting the number of strongly associated genes when studying susceptibility to the disease. The goal of this study was to compare computational tools with and without feature selection for predicting osteoporosis outcome in Taiwanese women based on genetic factors such as single nucleotide polymorphisms (SNPs). To elucidate relationships between osteoporosis and SNPs in this population, three classification algorithms were applied: multilayer feedforward neural network (MFNN), naive Bayes, and logistic regression. A wrapper-based feature selection method was also used to identify a subset of major SNPs. Experimental results showed that the MFNN model with the wrapper-based approach was the best predictive model for inferring disease susceptibility based on the complex relationship between osteoporosis and SNPs in Taiwanese women. The findings suggest that patients and doctors can use the proposed tool to enhance decision making based on clinical factors such as SNP genotyping data.
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Registered trials
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