Data mining with neural networks: solving business problems from application development to decision support
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining
Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining
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We conducted data mining method (association rule analysis) to elucidate the relationship between 6 lifestyles (overweight, drinking, smoking, meals, physical exercise, sleeping time, and meals), 5 family medical histories (hypertension, diabetes, cardiovascular disease, cerebrovascular disease, and liver disease), and 6 medical abnormalities (high blood pressure, hyperchoresterolemia, hypertrigriceridemia, high blood sugar, hyperuricemia, and liver dysfunction) in examination data using the medical examination data of 7 years, obtained from 5,350 male employees in the age group of 40-49 years. We found that number of combinations derived from data mining (association rule method) was greater than that derived from conventional method (logistic regression analysis). Moreover, values of both “confidence” and “odds ratio” derived from association rule were greater than that derived from logistic regression. We found that “the association rule method” was more and useful to elucidate effective combinations of risk factors in terms of lifestyle diseases.