@inproceedings{b7566c59c24447d3bea8f2403ada1027,
title = "Classifying osteosarcoma patients using machine learning approaches",
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.",
keywords = "Cancer, Machine Learning, Osteosarcoma, Random Forest, SVM",
author = "Zhi Li and Soroushmehr, {S. M.Reza} and Yingqi Hua and Min Mao and Yunping Qiu and Kayvan Najarian",
year = "2017",
month = sep,
day = "13",
doi = "10.1109/EMBC.2017.8036768",
language = "English (US)",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "82--85",
booktitle = "2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society",
note = "39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 ; Conference date: 11-07-2017 Through 15-07-2017",
}