The Application of Business Intelligence for Analysis and Prediction of Obesity Rates using The Decision Tree Method
DOI:
https://doi.org/10.33050/icit.v12i02.4431Keywords:
Business Intelligence, Decision Trees, Obesity, RapidMiner, Google Looker StudioAbstract
Obesity is a health issue influenced by diet, physical activity intensity, and lifestyle. This study aims to apply Business Intelligence (BI) to analyze and estimate the prevalence of obesity using the RapidMiner-based Decision Tree method and interactive visualization through Google Looker Studio. The dataset used consists of the Obesity Dataset, containing 1,610 entries and using 15 attributes. The model was tested using a 10-fold cross-validation approach and successfully achieved an accuracy of 71.55% ± 3.18%. The results of this study emphasize that height, age, and physical activity are the most important factors in obesity classification. In addition, the Google Looker Studio dashboard can present interactive data visualization, allowing users to easily explore obesity patterns related to lifestyle and health conditions. This study suggests that the combination of Decision Tree and Business Intelligence can increase efficiency in health data analysis.
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