Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Accurate Cancer Classification Using Expressions of Very Few Genes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Mining multiple comprehensible classification rules using genetic programming
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Risk prediction and risk factors identification from imbalanced data with RPMBGA+
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Empirical metabolite identification via GA feature selection and Bayes classification
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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In recent years, metabolic syndrome has emerged as a major health concern because it increases the risk of developing lifestyle diseases, such as diabetes, hypertension, and cardiovascular disease. Some of the symptoms of the metabolic syndrome are high blood pressure, decreased HDL cholesterol, and elevated triglycerides (TG). To prevent the developing of metabolic syndrome, accurate prediction of the future values of these health risk factors and identification of other factors from the health checkup and lifestyle data, which are highly related with these risk factors, are very important. In this paper, we propose a new framework, based on genetic algorithm and its variants, for identifying those important health factors and predicting the future health risk of a person with high accuracy. We show the effectiveness of the proposed system by applying it to the health checkup and lifestyle data of Toshiba Corporation.