Ada Boost Ensemble Classifier for Identification of Somatic Mutations
Anuradha Chokka1, K Sandhya Rani2
1Anuradha Chokka, Research Scholar, Department of Computer Science, Sri Padmavathi Mahila Visva Vidyalayam, Tirupati, Andhra Pradesh, India.
2Dr. K Sandhya Rani, Professor Department of Computer Science, Sri Padmavathi Mahila Visva Vidyalayam, Tirupati, Andhra Pradesh, India.
Manuscript received on 08 April 2019 | Revised Manuscript received on 15 April 2019 | Manuscript Published on 24 May 2019 | PP: 435-440 | Volume-8 Issue-6S3 April 2019 | Retrieval Number: F10880486S319/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Now a day’s most of the people are suffering from different kinds of cancer diseases, so the study of classification places a vital role in predicting the somatic mutations. As the size of gene variants and somatic mutations in the tumor increases, it is essential to predict the disease patterns using the machine learning models. Most of the traditional classification learning models are used to classify the somatic cancer based on related features. Also, the traditional classification algorithms predict only the existence of somatic cancer but not with high accuracy. In this proposed work, a novel framework which is the comparative study of accuracy computation is designed and implemented on the datasets to classify somatic mutations.
Keywords: Somatic Mutations, Machine Learning, Classification.
Scope of the Article: Classification