图 书 馆
脑卒中预测的集成机器学习方法An Integrated Machine Learning Approach to Stroke Prediction |
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| 课程网址: | http://videolectures.net/kdd2010_lee_imla/ |
| 主讲教师: | Honglak Lee |
| 开课单位: | 密歇根大学 |
| 开课时间: | 2010-10-01 |
| 课程语种: | 英语 |
| 中文简介: |  在美国，中风是第三大死亡原因，也是长期严重残疾的主要原因。准确预测中风对于早期干预和治疗非常有价值。在这项研究中，我们将Cox比例风险模型与针对心血管健康研究（CHS）数据集的中风预测的机器学习方法进行了比较。具体来说，我们考虑医学数据集中数据插补，特征选择和预测的常见问题。我们提出了一种新颖的自动特征选择算法，该算法基于我们提出的启发式算法：保守均值来选择鲁棒特征。与Cox比例风险模型和L1正则化Cox模型相比，结合支持向量机（SVM），我们提出的特征选择算法可在ROC曲线（AUC）下获得更大的面积。此外，我们提出了一种基于边际的删失回归算法，该算法结合了基于边际的分类器和删失回归的概念，以实现比Cox模型更好的一致性指标。总体而言，我们的方法在AUC指标和一致性指标方面都优于当前的最新水平。此外，我们的工作还确定了传统方法未发现的潜在风险因素。我们的方法可以用于其他疾病的临床预测，这些数据普遍缺乏并且对危险因素的了解还不够。 |
| 课程简介: | Stroke is the third leading cause of death and the principal cause of serious long-term disability in the United States. Accurate prediction of stroke is highly valuable for early intervention and treatment. In this study, we compare the Cox proportional hazards model with a machine learning approach for stroke prediction on the Cardiovascular Health Study (CHS) dataset. Specifically, we consider the common problems of data imputation, feature selection, and prediction in medical datasets. We propose a novel automatic feature selection algorithm that selects robust features based on our proposed heuristic: conservative mean. Combined with Support Vector Machines (SVMs), our proposed feature selection algorithm achieves a greater area under the ROC curve (AUC) as compared to the Cox proportional hazards model and L1 regularized Cox model. Furthermore, we present a margin-based censored regression algorithm that combines the concept of margin-based classifiers with censored regression to achieve a better concordance index than the Cox model. Overall, our approach outperforms the current state-of-the-art in both metrics of AUC and concordance index. In addition, our work has also identified potential risk factors that have not been discovered by traditional approaches. Our method can be applied to clinical prediction of other diseases, where missing data are common and risk factors are not well understood. |
| 关 键 词: | 比例风险模型; 鲁棒特征; 中风 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-05-08：zyk |
| 最后编审: | 2021-05-08：zyk |
| 阅读次数: | 61 |