Helforoush Zarindokht, Sayyad Hossein
Department of Mathematics and Systems Engineering, Florida Institute of Technology, Melbourne, FL, United States.
Independent Researcher, Melbourne, FL, United States.
Front Big Data. 2024 Sep 30;7:1469981. doi: 10.3389/fdata.2024.1469981. eCollection 2024.
As the global prevalence of obesity continues to rise, it has become a major public health concern requiring more accurate prediction methods. Traditional regression models often fail to capture the complex interactions between genetic, environmental, and behavioral factors contributing to obesity.
This study explores the potential of machine-learning techniques to improve obesity risk prediction. Various supervised learning algorithms, including the novel ANN-PSO hybrid model, were applied following comprehensive data preprocessing and evaluation.
The proposed ANN-PSO model achieved a remarkable accuracy rate of 92%, outperforming traditional regression methods. SHAP was employed to analyze feature importance, offering deeper insights into the influence of various factors on obesity risk.
The findings highlight the transformative role of advanced machine-learning models in public health research, offering a pathway for personalized healthcare interventions. By providing detailed obesity risk profiles, these models enable healthcare providers to tailor prevention and treatment strategies to individual needs. The results underscore the need to integrate innovative machine-learning approaches into global public health efforts to combat the growing obesity epidemic.
随着全球肥胖患病率持续上升，它已成为一个重大的公共卫生问题，需要更准确的预测方法。传统回归模型往往无法捕捉导致肥胖的遗传、环境和行为因素之间的复杂相互作用。
本研究探讨机器学习技术在改善肥胖风险预测方面的潜力。在进行全面的数据预处理和评估后，应用了各种监督学习算法，包括新型的人工神经网络 - 粒子群优化混合模型。
所提出的人工神经网络 - 粒子群优化模型实现了92%的显著准确率，优于传统回归方法。采用SHAP分析特征重要性，更深入地洞察各种因素对肥胖风险的影响。
研究结果凸显了先进机器学习模型在公共卫生研究中的变革性作用，为个性化医疗干预提供了一条途径。通过提供详细的肥胖风险概况，这些模型使医疗保健提供者能够根据个体需求调整预防和治疗策略。结果强调了将创新的机器学习方法纳入全球公共卫生努力以应对日益严重的肥胖流行的必要性。
