Gene-environment-lifestyle factors in breast cancer susceptibility: Machine Learning tools to build predictive models
- Jaykumar Patel
- Nasimeh Asgarian, AICML
- Paula Robson
- Russ Greiner, Dept of Computing Science; PI of AICML
- John Mackey, Cross Cancer Institute
- Sambasivarao Damaraju, Cross Cancer Institute
Methods: We collected data (378 features related to Diet/HLS/E) from 810 healthy subjects/576 Bca cases. We divided the data into training and validation sets. We used WEKA tools for implementation and tested 13 different algorithms.
Results and Conclusions: HLS/E factors as features (age, ethnicity, and type of food input, social involvement, traveling, physical activity and body measurement) produced a good predictive model; Bayes Network in the training (10-fold cross validation) and validation sets showed an accuracy of 87.65% and 95.68% respectively. Serum profiling (molecular/ metabolome) of the subjects may help to gain mechanistic insights to disease etiology. Our model will potentially aid in screening of individuals who are predisposed to breast cancer risk.
Citation
J. Patel, N. Asgarian, P. Robson, R. Greiner, J. Mackey, S. Damaraju. " Gene-environment-lifestyle factors in breast cancer susceptibility: Machine Learning tools to build predictive models". Journal of Carcinogenesis, 14(Suppl 1), pp S16-S20, January 2015.Keywords: | machine learning, breast cancer, lifestyle factors, screening, medical informatics |
Category: | In Journal |
Web Links: | JournalURL |
BibTeX
@article{Patel+al:15, author = {Jaykumar Patel and Nasimeh Asgarian and Paula Robson and Russ Greiner and John Mackey and Sambasivarao Damaraju}, title = { Gene-environment-lifestyle factors in breast cancer susceptibility: Machine Learning tools to build predictive models}, Volume = "14", Number = {Suppl 1}, Pages = {S16-S20}, journal = {Journal of Carcinogenesis}, year = 2015, }Last Updated: February 10, 2020
Submitted by Sabina P