A Comprehensive Study of Machine Learning Models in Radiogenomics
Abstract: The ever increasing medical data has led to an increasing interest and demand for a personalized treatment setup in which each individual has its own personalized treatment plan. Specifically talking, Radiation Oncology has generated a lot of input as well as output data through which it has been able to capture the interest of the Machine Learning Methodologies. Going further, Radiogenomics, in particular, the study of genetic variation associated to radiation has been seen as a potentiate user of a lot of Machine Learning approaches. Currently, uniform doses specific to the tumor are being used. The contribution of genetics to radiations far exceeds the current understanding of risk variants. In this paper, we study the applications of Machine Learning in the Radiogenomics field which have been compared and contrasted to overcome the shortcomings of the current situation.
Keywords: Radiogenomics, Machine Learning, Personalized Treatment, Radiation Oncology
How to Cite?: Eash Sharma, Ashwin Garg, "A Comprehensive Study of Machine Learning Models in Radiogenomics", Volume 7 Issue 8, August 2018, International Journal of Science and Research (IJSR), Pages: 10-13, https://www.ijsr.net/getabstract.php?paperid=ART2019379, DOI: https://dx.doi.org/10.21275/ART2019379