1. Intelligent Database Systems Lab N.Y.U.S.T. I. M. An Integrated Machine Learning Approach to Stroke Prediction Presenter: Tsai Tzung Ruei Authors: Aditya Khosla, Yu Cao, Cliff Chiung-Yu Lin, Hsu- Kuang Chiu, Junling Hu, Honglak Lee SIGKDD 2010 國立雲林科技大學 National Yunlin University of Science and Technology

2. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Outline Motivation Objective Methodology Experiments Conclusion Comments 2

3. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Motivation Most previous prediction models have adopted features (risk factors) that are verified by clinical trials or selected manually by medical experts. In the past, high-performance machine learning algorithms such as SVM and logistic regression were not explored. 3

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objective To propose a novel automatic feature selection algorithm that selects robust features based on our proposed heuristic: conservative mean. To 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. 4

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology 5

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Conservative mean feature selection  To consider the variance across different folds along with the average of the prediction performance.  To evaluate the performance of each feature individually. 6 Age Calculated hypertensi on status Left ventricular mass

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Conservative mean feature selection 7 VECTOR Age Left ventricular mass Calculated hypertension status

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Learning Algorithms for Prediction  Margin-based Censored Regression 8 SVM True False

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments Data Imputation Feature Selection 9

10. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments Stroke Prediction 10

11. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments Identifying risk factors 11

12. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusion Contribution  An extensive evaluation of the problems of data imputation, feature selection and prediction in medical data, with comparisons against the Cox proportional hazards model.  A novel feature selection algorithm, Conservative Mean feature selection, that outperforms both L 1 regularized Cox model and L 1 regularized logistic regression on the CHS dataset.  A novel risk prediction algorithm, Margin-based Censored Regression, that outperforms the Cox model given the same set of features. 12

13. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Comments Advantage  The structure of this paper is very clear. Drawback  …… Application  classification 13