Optimization of Body Mass Index Classification Using Machine Learning Approach for Early Detection of Obesity Risk

Abstract

This study aims to optimize the classification of obesity risk at an early stage using Principal Component Analysis (PCA), which is an important technique in machine learning. PCA is used to reduce the dimensionality of data, maintain important information without losing data, and has the advantage of reducing complexity which usually increases the risk of overfitting. The obesity dataset will be classified using algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, Gradient Boosting Linear, and XGBoost. Specifically, each algorithm is chosen because of its respective advantages: KNN for nonlinear data, SVM for high-dimensional data, and Random Forest and XGBoost for complex data patterns. Evaluation is carried out using metrics such as accuracy, precision, recall, and F1-score to assess the performance of the algorithm. The results show that the Random Forest and XGBoost algorithms provide the best performance in terms of accuracy, especially when all dataset features are used without PCA reduction. This study is expected to be a consideration in determining the best algorithm for obesity classification, supporting early detection, and facilitating decision making in health analysis.