对比两种集成方法，发现堆叠模型在肥胖风险预测中更准确。
Ensemble Learning for Healthcare: A Comparative Analysis of Hybrid Voting and Ensemble Stacking in Obesity Risk Prediction
- 用堆叠和加权投票法融合多个机器学习模型提升预测效果
- 堆叠在两个数据集上均表现更优，最高准确率达98.98%
- 适合医疗健康领域需要高精度预测的场景
肥胖是受饮食、生理与环境因素驱动的重大全球健康问题，与糖尿病、心血管疾病及癌症密切相关。机器学习为早期肥胖风险预测提供了新路径，但对集成方法——特别是混合多数投票与集成堆叠——的比较研究仍有限。本研究对比了两种集成策略在肥胖风险预测中的表现，旨在确定哪种方法更具准确性与效率。采用两个数据集，评估三种集成模型：多数硬投票、加权硬投票，以及以多层感知机为元分类器的堆叠模型。从九种机器学习算法中，通过50组超参数配置筛选出表现最优的前三名作为基学习器。数据预处理包括平衡化与异常值检测，性能评估指标为准确率与F1分数。在数据集1上，加权硬投票与堆叠表现相近（准确率：0.920304，F1：0.920070），优于多数硬投票；在数据集2上，堆叠显著领先（准确率：0.989837，F1：0.989825），优于多数硬投票（准确率：0.981707，F1：0.981675）和加权硬投票（表现最差）。结果表明，集成堆叠在复杂数据分布下具备更强预测能力，而混合多数投票仍具鲁棒性。
原文摘要 · Abstract (English)
Obesity is a critical global health issue driven by dietary, physiological, and environmental factors, and is strongly associated with chronic diseases such as diabetes, cardiovascular disorders, and cancer. Machine learning has emerged as a promising approach for early obesity risk prediction, yet a comparative evaluation of ensemble techniques -- particularly hybrid majority voting and ensemble stacking -- remains limited. This study aims to compare hybrid majority voting and ensemble stacking methods for obesity risk prediction, identifying which approach delivers higher accuracy and efficiency. The analysis seeks to highlight the complementary strengths of these ensemble techniques in guiding better predictive model selection for healthcare applications. Two datasets were utilized to evaluate three ensemble models: Majority Hard Voting, Weighted Hard Voting, and Stacking (with a Multi-Layer Perceptron as meta-classifier). A pool of nine Machine Learning (ML) algorithms, evaluated across a total of 50 hyperparameter configurations, was analyzed to identify the top three models to serve as base learners for the ensemble methods. Preprocessing steps involved dataset balancing, and outlier detection, and model performance was evaluated using Accuracy and F1-Score. On Dataset-1, weighted hard voting and stacking achieved nearly identical performance (Accuracy: 0.920304, F1: 0.920070), outperforming majority hard voting. On Dataset-2, stacking demonstrated superior results (Accuracy: 0.989837, F1: 0.989825) compared to majority hard voting (Accuracy: 0.981707, F1: 0.981675) and weighted hard voting, which showed the lowest performance. The findings confirm that ensemble stacking provides stronger predictive capability, particularly for complex data distributions, while hybrid majority voting remains a robust alternative.
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