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Machine Learning for Obesity Prediction by Abbs Alazhari is a document available to read on EtoBox.

The study aimed to develop an interpretable machine learning algorithm to predict overweight and obesity risk in adults using a combination of classical techniques. Data from 1179 participants in Madrid were analyzed, revealing that a cascade classifier model combining gradient boosting, random forest, and logistic regression achieved the highest accuracy (79%), precision (84%), and recall (89%). Key predictive factors included age, sex, academic level, profession, smoking habits, wine consumption, and adhe

Author

Abbs Alazhari

Language

EN