Classifying clinically actionable genetic mutations using KNN and SVM

CHIVUKULA, Rohit, TANGIRALA, Jaya Lakshmi, UDAY, Sanku Satya and PAVANI, Satti Thanuja (2021). Classifying clinically actionable genetic mutations using KNN and SVM. Indonesian Journal of Electrical Engineering and Computer Science, 24 (3), 1672-1679.

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Abstract

Cancer is one of the major causes of death in humans. Early diagnosis of genetic mutations that cause cancer tumor growth leads to personalized medicine to the decease and can save the life of majority of patients. With this aim, Kaggle has conducted a competition to classify clinically actionable gene mutations based on clinical evidence and some other features related to gene mutations. The dataset contains 3321 training data points that can be classified into 9 classes. In this work, an attempt is made to classify these data points using K-nearest neighbors (KNN) and linear support vector machines (SVM) in a multi class environment. As the features are categorical, one hot encoding as well as response coding are applied to make them suitable to the classifiers. The prediction performance is evaluated using log loss and KNN has performed better with a log loss value of 1.10 compared to that of SVM 1.24.

Item Type:	Article
Uncontrolled Keywords:	4009 Electronics, sensors and digital hardware; 4606 Distributed computing and systems software
Identification Number:	https://doi.org/10.11591/ijeecs.v24.i3.pp1672-1679
Page Range:	1672-1679
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	01 Mar 2024 10:41
Last Modified:	01 Mar 2024 10:52
URI:	https://shura.shu.ac.uk/id/eprint/33299

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