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 language | English (US) |
|---|---|
| Title of host publication | 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
| Subtitle of host publication | Smarter Technology for a Healthier World, EMBC 2017 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 82-85 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781509028092 |
| DOIs | |
| State | Published - Sep 13 2017 |
| Event | 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, Korea, Republic of Duration: Jul 11 2017 → Jul 15 2017 |
Other
| Other | 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 |
|---|---|
| Country | Korea, Republic of |
| City | Jeju Island |
| Period | 7/11/17 → 7/15/17 |
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Keywords
- Cancer
- Machine Learning
- Osteosarcoma
- Random Forest
- SVM
ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics
Cite this
Classifying osteosarcoma patients using machine learning approaches. / Li, Zhi; Soroushmehr, S. M.Reza; Hua, Yingqi; Mao, Min; Qiu, Yunping; Najarian, Kayvan.
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 82-85 8036768.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Classifying osteosarcoma patients using machine learning approaches
AU - Li, Zhi
AU - Soroushmehr, S. M.Reza
AU - Hua, Yingqi
AU - Mao, Min
AU - Qiu, Yunping
AU - Najarian, Kayvan
PY - 2017/9/13
Y1 - 2017/9/13
N2 - 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.
AB - 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.
KW - Cancer
KW - Machine Learning
KW - Osteosarcoma
KW - Random Forest
KW - SVM
UR - http://www.scopus.com/inward/record.url?scp=85032212731&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85032212731&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2017.8036768
DO - 10.1109/EMBC.2017.8036768
M3 - Conference contribution
C2 - 29059816
AN - SCOPUS:85032212731
SP - 82
EP - 85
BT - 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
ER -