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The random forest classification algorithm exhibits superior accuracy rates of 97% for Obesity_Type_I, 99% for Obesity_Type_II, and 83% for Obesity_Type_III compared to alternative classifiers.
Practitioners can utilize machine learning algorithms, particularly random forests, for more accurate obesity classification.
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The random forest classification algorithm exhibits superior accuracy rates of 97% for Obesity_Type_I, 99% for Obesity_Type_II, and 83% for Obesity_Type_III compared to alternative classifiers.
Why this rating
The study employs a benchmark dataset and various machine learning methods, indicating a robust analysis.
Source
Detection and Diagnosis of Different Types of Obesity Using Machine Learning-Based Approaches
Yogesh Kumar et al. · 2023
DOI 10.1109/eiceeai60672.2023.10590462
otherCited 36×
Read the paper DOI resolved against Crossref · corpus check 2026-06-10
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