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Obesity Level Prediction Based on Data Mining Techniques


Asma Alqahtani, Fatima Albuainin, Rana Alrayes, Noura Al muhanna, Eyman Alyahyan, and Ezaz Aldahasi


Vol. 21  No. 3  pp. 103-111


Obesity affects individuals of all gender and ages worldwide; consequently, several studies have performed great works to define factors causing it. This study develops an effective method to trace obesity levels based on supervised data mining techniques such as Random Forest and Multi-Layer Perception (MLP), so as to tackle this universal epidemic. Notably, the dataset was from countries like Mexico, Peru, and Colombia in the 14- 61year age group, with varying eating habits and physical conditions. The data includes 2111 instances and 17 attributes labelled using NObesity, which facilitates categorization of data using Overweight Levels l I and II, Insufficient Weight, Normal Weight, as well as Obesity Type I to III. This study found that the highest accuracy was achieved by Random Forest algorithm in comparison to the MLP algorithm, with an overall classification rate of 96.7%.


Obesity, Data Mining, prediction, Multilayer Perceptron (MLP), Random Forest.