Classification of Cancerous Profiles using Machine Learning Algorithms
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International Journal of Computer Trends and Technology (IJCTT) |  |
| © 2019 by IJCTT Journal | ||
| Volume-67 Issue-3 |
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| Year of Publication : 2019 | ||
| Authors : Yaramala Sushma, Vanitha Kakollu | ||
| DOI : 10.14445/22312803/IJCTT-V67I3P119 |
MLA Style: Yaramala Sushma, Vanitha Kakollu, "Classification of Cancerous Profiles using Machine Learning Algorithms" International Journal of Computer Trends and Technology 67.3 (2019): 99-101.
APA Style:Yaramala Sushma, Vanitha Kakollu, (2019). Classification of Cancerous Profiles using Machine Learning Algorithms. International Journal of Computer Trends and Technology, 67(3), 99-101.
Abstract
Many existing methods are available for lung cancer identification. This type of treatment recommended for an individual is influenced by various factors such as cancer-type, the severity of cancer (stage) and most important the genetic heterogeneity. In such a complex environment, the targeted drug treatments are likely to be irresponsive or respond differently. In order to study anticancer drug response we need to understand cancerous profiles. These cancerous profiles carry information which can explore the underlying factors responsible for cancer growth. Hence, there is need to analyse cancer data for predicting optimal treatment options. Analysis of such profiles can help to predict and discover potential drug targets and drugs. In this paper the main aim is to provide machine learning based classification technique for cancerous profiles.
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Keywords
Cancer, Machine Learning, SVM, Random Forest, k NN, Genes, Drug prediction.