1 / 13

An Integrated Machine Learning Approach to Stroke Prediction

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 . 國立雲林科技大學 National Yunlin University of Science and Technology. SIGKDD 2010. Outline. Motivation

seoras
Download Presentation

An Integrated Machine Learning Approach to Stroke Prediction

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. An Integrated Machine Learning Approachto Stroke Prediction Presenter: Tsai TzungRuei Authors: AdityaKhosla, Yu Cao, Cliff Chiung-Yu Lin, Hsu-Kuang Chiu, JunlingHu, Honglak Lee 國立雲林科技大學 National Yunlin University of Science and Technology SIGKDD 2010

  2. Outline • Motivation • Objective • Methodology • Experiments • Conclusion • Comments

  3. Motivation • Most previous prediction models have adopted features (risk factors) that are verified by clinical trials or selected manuallyby medical experts. • In the past, high-performance machine learning algorithms such as SVM and logistic regression were not explored.

  4. Objective • To propose a novelautomatic 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 indexthan the Cox model.

  5. Methodology

  6. Methodology • Conservative mean feature selection • To consider the varianceacross different folds along with the average of the prediction performance. • To evaluate the performanceof each feature individually.

  7. Age Methodology • Conservative mean feature selection • Left ventricular mass • Calculated hypertension status VECTOR

  8. Methodology • Learning Algorithms for Prediction • Margin-based Censored Regression SVM

  9. Experiments • Data Imputation • Feature Selection

  10. Experiments • Stroke Prediction

  11. Experiments • Identifying risk factors

  12. Conclusion • Contribution • An extensive evaluation of the problems of data imputation, feature selectionand prediction in medical data, with comparisons against the Cox proportional hazardsmodel. • A novelfeature 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 giventhe same set of features.

  13. Comments • Advantage • The structure of this paper is very clear. • Drawback • …… • Application • classification

More Related