2010 AnIntegratedMachineLearningAppr

• (Khosla et al., 2010) ⇒ Aditya Khosla, Yu Cao, Cliff Chiung-Yu Lin, Hsu-Kuang Chiu, Junling Hu, and Honglak Lee. (2010). “An Integrated Machine Learning Approach to Stroke Prediction.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835830

Subject Headings:

Notes

• Categories and Subject Descriptors: J.3 Computer Application: Life and Medical Sciences; I.2.6 Artificial Intelligence: Learning; I.5.2 Pattern Recognition: Design Methodology.
• General Terms: Experimentation, Algorithms, Performance.

Cited By

• http://scholar.google.com/scholar?q=%22An+integrated+machine+learning+approach+to+stroke+prediction%22+2010
• http://portal.acm.org/citation.cfm?id=1835830&preflayout=flat#citedby

Quotes

Abstract

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 AnIntegratedMachineLearningAppr	Aditya Khosla Yu Cao Cliff Chiung-Yu Lin Hsu-Kuang Chiu Junling Hu Honglak Lee			An Integrated Machine Learning Approach to Stroke Prediction		KDD-2010 Proceedings		10.1145/1835804.1835830		2010