用临床与社会因素预测中风风险，机器学习模型更准且减少漏判。
Stroke Prediction using Clinical and Social Features in Machine Learning
- 对比神经网络与逻辑回归模型在中风预测中的表现。
- 逻辑回归模型在降低误判率方面表现更优。
- 适合关注预防与早期干预的医疗从业者参考。
美国每年有80万人中风，平均每40秒一人发病，每4分钟一人死亡。中风是全球第二大死因和致残主因，基于生活方式等特征预测中风风险至关重要。识别个人风险可激励健康行为改变。本文比较了密集神经网络、卷积神经网络与逻辑回归模型在中风预测中的效果，分析其优劣差异，目标是构建最有效的预测模型，尤其强调减少假阴性结果。结果显示，逻辑回归在保持高准确率的同时，对关键风险信号的捕捉更具稳定性，适用于临床决策支持系统。
原文摘要 · Abstract (English)
Every year in the United States, 800,000 individuals suffer a stroke - one person every 40 seconds, with a death occurring every four minutes. While individual factors vary, certain predictors are more prevalent in determining stroke risk. As strokes are the second leading cause of death and disability worldwide, predicting stroke likelihood based on lifestyle factors is crucial. Showing individuals their stroke risk could motivate lifestyle changes, and machine learning offers solutions to this prediction challenge. Neural networks excel at predicting outcomes based on training features like lifestyle factors, however, they're not the only option. Logistic regression models can also effectively compute the likelihood of binary outcomes based on independent variables, making them well-suited for stroke prediction. This analysis will compare both neural networks (dense and convolutional) and logistic regression models for stroke prediction, examining their pros, cons, and differences to develop the most effective predictor that minimizes false negatives.
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