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Detecting malignant patients via modified boosted tree

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Abstract

As one of the most effective measures to extract useful information from medical database and provide scientific decision-making for diagnosis and treatment of diseases, medical data mining has become an increasingly hot topic in the last few years. Some of the intrinsic characteristics of medical databases, such as the huge volume and imbalanced samples as well as stringent performance standards, make this mining process particularly challenging. By elaborating various challenges existing in Task 1 of KDD Cup 2008 competition, this paper analyzes some potential solutions to these problems and presents a modified boosted tree as the final classification model. This model ranked the fourth among all the solutions to Task 1. We hope that our analysis and solutions to these challenges would contribute to the development of medical data mining applications.

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Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University, Jinan, 250101, China

    CaiLing Dong & YiLong Yin

  2. College of Information and Communication, Harbin Engineering University, Harbin, 150001, China

    XiuKun Yang

Authors
  1. CaiLing Dong
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  2. YiLong Yin
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  3. XiuKun Yang
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Correspondence to YiLong Yin.

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Dong, C., Yin, Y. & Yang, X. Detecting malignant patients via modified boosted tree. Sci. China Inf. Sci. 53, 1369–1378 (2010). https://doi.org/10.1007/s11432-010-3107-9

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  • DOI: https://doi.org/10.1007/s11432-010-3107-9

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