Obesity Prediction with Classification Model Based on Anthropometric Data and Lifestyle Factors using Random Forest
DOI:
https://doi.org/10.56127/juit.v4i2.2084Keywords:
Machine Learning, Model, Algoritma Random Forest, Dataset, Klasifikasi, PrediksiAbstract
Overweight and obesity have become a significant health problem worldwide, including in Indonesia. This study aims to solve this problem by developing a machine learning model based on the Random Forest Classifier algorithm. This model can be used to classify various types of obesity based on relevant variables. This study uses data from the trusted Kaggle data source to find and manage community problems with obesity and overweight. The machine learning approach allows this model to predict obesity types with high accuracy, exceeding 90%. The advantage of this approach is that it can create solutions based on a person's gender, age, weight, and height, as well as other relevant factorsReferences
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