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<p>Rising concerns over public health and chronic disease prevalence have intensified the demand for data-driven, personalized fitness interventions. While national health programs offer general guidelines, they often lack the granularity required to address individual variability in health status, lifestyle, and demographic context. This paper presents a machine learning framework to generate personalized fitness recommendations aligned with national health goals. Leveraging population-scale data, the aim is to optimize physical activity planning while maintaining fairness and clinical relevance across demographic subgroups. The study utilizes the National Health and Nutrition Examination Survey (NHANES) dataset, integrating biometric, behavioral, and demographic features. To enhance the behavioral relevance of our predictions, we integrated supplemental variables from the Behavioral Risk Factor Surveillance System (BRFSS), capturing psychological, motivational, and environmental factors that influence physical activity adherence. After preprocessing, models were developed using XGBoost, Decision Trees, and Artificial Neural Networks. Both regression (to estimate weekly activity minutes) and classification (to assign risk groups) tasks were addressed. Performance was evaluated through MeanIoU, Dice Score, sensitivity, and specificity. Demographic fairness was assessed via subgroup residuals and fairness gap analysis. XGBoost achieved superior performance, with a MeanIoU of 0.789 and F1 scores exceeding 0.79 across all risk categories. Model consistency was observed across age, gender, and ethnicity, with fairness gaps below 0.05. Residual error analysis and risk classification confirmed high reliability and low variance. The proposed system demonstrates the feasibility of using AI to personalize fitness plans at scale. It offers a pathway to integrate precision fitness with national policy, supporting equitable and effective public health strategies.</p>

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Personalized fitness recommendations using machine learning for optimized national health strategy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月24日

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  • Juan Chen,
  • Yan Wang

摘要

Rising concerns over public health and chronic disease prevalence have intensified the demand for data-driven, personalized fitness interventions. While national health programs offer general guidelines, they often lack the granularity required to address individual variability in health status, lifestyle, and demographic context. This paper presents a machine learning framework to generate personalized fitness recommendations aligned with national health goals. Leveraging population-scale data, the aim is to optimize physical activity planning while maintaining fairness and clinical relevance across demographic subgroups. The study utilizes the National Health and Nutrition Examination Survey (NHANES) dataset, integrating biometric, behavioral, and demographic features. To enhance the behavioral relevance of our predictions, we integrated supplemental variables from the Behavioral Risk Factor Surveillance System (BRFSS), capturing psychological, motivational, and environmental factors that influence physical activity adherence. After preprocessing, models were developed using XGBoost, Decision Trees, and Artificial Neural Networks. Both regression (to estimate weekly activity minutes) and classification (to assign risk groups) tasks were addressed. Performance was evaluated through MeanIoU, Dice Score, sensitivity, and specificity. Demographic fairness was assessed via subgroup residuals and fairness gap analysis. XGBoost achieved superior performance, with a MeanIoU of 0.789 and F1 scores exceeding 0.79 across all risk categories. Model consistency was observed across age, gender, and ethnicity, with fairness gaps below 0.05. Residual error analysis and risk classification confirmed high reliability and low variance. The proposed system demonstrates the feasibility of using AI to personalize fitness plans at scale. It offers a pathway to integrate precision fitness with national policy, supporting equitable and effective public health strategies.