Analysis Between Lifestyle, Family Medical History and Medical Abnormalities Using Data Mining Method – Association Rule Analysis

• Published in: Knowledge-Based Intelligent Information and Engineering Systems pp. 161 - 171
• Main Authors: Ogasawara, Mitsuhiro, Sugimori, Hiroki, Iida, Yukiyasu, Yoshida, Katsumi
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg
• Series: Lecture Notes in Computer Science
• Subjects: data mining; family medical history; health checkups; lifestyle; medical abnormality
• ISBN: 3540288953; 9783540288954
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11552451_22

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.