Title: Stroke Prediction using Clinical and Social Features in Machine Learning Show Chinese title

Title: 使用临床和社会特征的机器学习中风预测

Abstract: 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. Show Chinese abstract

Abstract: 在美国，每年有 800,000 人遭受中风——每 40 秒就有一人，每四分钟就有一人死亡。 尽管个体因素各不相同，但某些预测因子在确定中风风险方面更为普遍。 由于中风是全球死亡和残疾的第二大原因，根据生活方式因素预测中风的可能性至关重要。 向个人展示他们的中风风险可能激励生活方式的改变，而机器学习提供了应对这一预测挑战的解决方案。 神经网络擅长基于训练特征（如生活方式因素）预测结果，然而，它们并非唯一的选择。 逻辑回归模型也可以根据自变量有效计算二元结果的可能性，使其非常适合中风预测。 本分析将比较用于中风预测的神经网络（密集型和卷积型）和逻辑回归模型，研究它们的优点、缺点和差异，以开发出最有效的预测器，最小化假阴性。