document: An integrated machine learning approach to stroke prediction 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 feature selection algorithm . 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 .
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