CLASSIFICAÇÃO DE NÍVEIS DE OBESIDADE POR MODELOS DE MACHINE LEARNING: COMPARAÇÃO ENTRE RANDOM FOREST, SVM E REGRESSÃO LOGÍSTICA SOB UMA PERSPECTIVA DE INTELIGÊNCIA ARTIFICIAL CLÍNICA
DOI:
https://doi.org/10.56238/arev7n12-107Palavras-chave:
Machine Learning, Inteligência Artificial, Obesidade, Random Forest, Saúde DigitalResumo
O aumento global da prevalência de obesidade tem impulsionado o desenvolvimento de ferramentas analíticas capazes de aprimorar o diagnóstico e a estratificação de risco. Este estudo investiga a aplicação de três modelos de Machine Learning (Random Forest, Support Vector Machine e Regressão Logística Multinomial) para a classificação de níveis de obesidade em adultos. O pipeline proposto inclui pré-processamento, imputação, codificação categórica, normalização, validação cruzada e avaliação multicritério. O estudo incorpora técnicas modernas de interpretabilidade baseadas em Permutation Importance, permitindo quantificar o impacto de cada variável na métrica F1-macro sob perspectiva de Inteligência Artificial aplicada à saúde. Implementou-se também baseline clínico baseado exclusivamente no IMC (Índice de Massa Corporal), possibilitando comparar métodos estatísticos tradicionais com abordagens supervisionadas. Os resultados demonstram melhor desempenho do Random Forest, superando significativamente o baseline clínico e os demais modelos. Os achados evidenciam o potencial do Machine Learning como ferramenta auxiliar em saúde digital, oferecendo previsões mais robustas do que regras simplificadas.
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