Classifying osteosarcoma patients using machine learning approaches

Zhi Li, S. M.Reza Soroushmehr, Yingqi Hua, Min Mao, Yunping Qiu, Kayvan Najarian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Metabolomic data analysis presents a unique opportunity to advance our understanding of osteosarcoma, a common bone malignancy for which genomic and proteomic studies have enjoyed limited success. One of the major goals of metabolomic studies is to classify osteosarcoma in early stages, which is required for metastasectomy treatment. In this paper we subject our metabolomic data on osteosarcoma patients collected by the SJTU team to three classification methods: logistic regression, support vector machine (SVM) and random forest (RF). The performances are evaluated and compared using receiver operating characteristic curves. All three classifiers are successful in distinguishing between healthy control and tumor cases, with random forest outperforming the other two for cross-validation in training set (accuracy rate for logistic regression, support vector machine and random forest are 88%, 90% and 97% respectively). Random forest achieved overall accuracy rate of 95% with 0.99 AUC on testing set.

Original languageEnglish (US)
Title of host publication2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publicationSmarter Technology for a Healthier World, EMBC 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages82-85
Number of pages4
ISBN (Electronic)9781509028092
DOIs
StatePublished - Sep 13 2017
Event39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, Korea, Republic of
Duration: Jul 11 2017Jul 15 2017

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
CountryKorea, Republic of
CityJeju Island
Period7/11/177/15/17

Keywords

  • Cancer
  • Machine Learning
  • Osteosarcoma
  • Random Forest
  • SVM

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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  • Cite this

    Li, Z., Soroushmehr, S. M. R., Hua, Y., Mao, M., Qiu, Y., & Najarian, K. (2017). Classifying osteosarcoma patients using machine learning approaches. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings (pp. 82-85). [8036768] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2017.8036768