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Tlalpan 2020 Case Study: Enhancing Uric Acid Level Prediction with Machine Learning Regression and Cross-Feature

Guadalupe Gutiérrez-Esparza1,2, Mireya Martínez-García3, Manlio F Márquez-Murillo2

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Machine learning models accurately predict uric acid levels by analyzing clinical, lifestyle, and nutritional data. Key predictors for hyperuricemia differ between men and women, highlighting the need for personalized health strategies.

Keywords:
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Area of Science:

  • Metabolic health and disease prediction
  • Biomarker analysis and machine learning applications

Background:

  • Uric acid is a metabolic byproduct with dual roles: antioxidant at physiological levels and a contributor to diseases like gout and cardiovascular issues when elevated.
  • Hyperuricemia is linked to metabolic disorders, including hypertension and insulin resistance, underscoring the need to understand its regulation.

Purpose of the Study:

  • To predict uric acid levels using machine learning algorithms.
  • To identify key clinical, anthropometric, lifestyle, and nutritional variables associated with hyperuricemia.

Main Methods:

  • Application of Boosted Decision Trees (Boosted DTR), eXtreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost) models.
  • Utilized Shapley Additive Explanations (SHAP) for variable importance.
  • Employed feature engineering and cross-feature selection to enhance model performance, evaluated by MSE, RMSE, and R².

Main Results:

  • XGBoost excelled with anthropometric/clinical data; CatBoost identified nutritional risk factors.
  • Distinct gender-specific predictive profiles for uric acid levels were observed.
  • Men's levels influenced by renal function, lipids, and paternal history; women's by metabolic/cardiovascular markers and lifestyle.

Conclusions:

  • Machine learning models effectively predict uric acid levels and identify key determinants.
  • Findings reveal distinct metabolic, nutritional, and lifestyle factors influencing uric acid in men and women.
  • Supports targeted public health strategies for hyperuricemia prevention based on gender-specific insights.