Performance Optimization of Adaboost and XGBoost Algorithms on Obesity Disease Dataset with Smote Oversampling Technique

Obesity is a condition where a person's body weight exceeds the normal limit due to the accumulation of excessive fat tissue. Thus, obesity is considered a global public health challenge. This is proven by the latest data from the World Health Organization (WHO) in 2022, namely that as many as 2.5 billion adults aged 18 years and over are overweight and 890 million of them suffer from obesity. Therefore, it is critical to accurately identify these risk factors in order to implement effective interventions in the prevention and management of obesity. This research uses the AdaBoost and XGBoost algorithms. This research stage begins with problem identification, data collection, preprocessing and model evaluation and model comparison. This research also applies the SMOTE technique to balance unbalanced data. Based on the results of research that has been carried out, it shows that XGBoost with SMOTE data tends to be higher than the results of other model evaluations. The accuracy and recall values are 0.945 and precision 0.947. Meanwhile, the lowest accuracy and recall values are found in AdaBoost with a SMOTE of 0.388. Apart from that, the lowest precision value is found in AdaBoost without SMOTE, namely 0.403.