School of Health Management, Zaozhuang University, Zaozhuang, 277000, China.
School of Public Health and Nursing, Hangzhou Normal University, Hangzhou, 311121, China.
Sci Rep. 2024 Sep 28;14(1):22424. doi: 10.1038/s41598-024-73826-6.
Obesity is closely associated with various chronic diseases.Therefore, accurate, reliable and cost-effective methods for preventing its occurrence and progression are required. In this study, we developed a visualized obesity risk prediction system based on machine learning techniques, aiming to achieve personalized comprehensive health management for obesity. The system utilized a dataset consisting of 1678 anonymized health examination records, including individual lifestyle factors, body composition, blood routine, and biochemical tests. Ten multi-classification machine learning models, including Random Forest and XGBoost, were constructed to identify non-obese individuals (BMI < 25), class 1 obese individuals (25 ≤ BMI < 30), and class 2 obese individuals (30 ≤ BMI). By evaluating the performance of each model on the test set, we selected XGBoost as the best model and built the visualized obesity risk prediction system based on it. The system exhibited good predictive performance and interpretability, directly providing users with their obesity risk levels and determining corresponding intervention priorities. In conclusion, the developed obesity risk prediction system possesses high accuracy and interactivity, aiding physicians in formulating personalized health management plans and achieving comprehensive and accurate obesity management.
肥胖与各种慢性疾病密切相关。因此，需要准确、可靠且具有成本效益的方法来预防肥胖的发生和发展。在本研究中，我们开发了一种基于机器学习技术的可视化肥胖风险预测系统，旨在实现肥胖的个性化综合健康管理。该系统利用包含 1678 份匿名健康检查记录的数据集，包括个体生活方式因素、身体成分、血常规和生化检查。构建了十个多分类机器学习模型，包括随机森林和 XGBoost，以识别非肥胖个体（BMI<25）、1 类肥胖个体（25≤BMI<30）和 2 类肥胖个体（30≤BMI）。通过评估每个模型在测试集上的性能，我们选择 XGBoost 作为最佳模型，并基于它构建了可视化肥胖风险预测系统。该系统具有良好的预测性能和可解释性，直接向用户提供其肥胖风险水平，并确定相应的干预优先级。总之，开发的肥胖风险预测系统具有高精度和交互性，有助于医生制定个性化的健康管理计划，实现全面、准确的肥胖管理。
