Abstract
Time-series data analyses of blood sugar and HbA1c levels of a diabetic were presented. These analyses were based on the simple idea that accumulation of the effects of a person's lifestyle could influence daily health with some delay, which may reflect complex bio-reactions in the human body. In these analyses, correlations of time-series data are checked focusing on the accumulation of effects of daily lifestyle, and on variation of daily health data. A retardation parameter is introduced. Rule-mining using the C5.0 algorithm following the time-series data analyses produced useful relationships among lifestyles, blood sugar level, and HbAlc level of a diabetic.
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