Association Rule Mining-Based Food Preferences Analysis Using FP-Growth Method

In today’s fast-paced lifestyle, many overlook food quality, leading to high fast-food consumption and unhealthy eating habits. This can result in health issues. Hence, it has become essential for people to have a balanced, nutritional, healthy diet to deal with those issues. Food preference analysis can benefit from applying predictive analysis methods to uncover consumption patterns and their relationship with nutritional risk factors. This study aims to explore the association between food preferences and BMI using the FP-Growth algorithm from the Association Rules Mining method, which follows the CRISP-DM methodology. As a result, strong rules are being created from the high support and confidence in modeling process. This study is significant in the field of food preferences analysis.