Comparison of supervised models in hepatocellular carcinoma tumor classification based on expression data using principal component analysis (PCA)

Anggrainy Togi Marito Siregar, Titin Siswantining, Alhadi Bustamam, Devvi Sarwinda

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Hepatocellular Carcinoma is one of the cancer disease cases with a high dead population. To know that someone is affected by Hepatocellular Carcinoma Tumor by observing the expression of genes on DNA. Gene expression obtained from the microarray laboratory tool that produced genes probe. In this case, there are 54675 gene expressions with 40 samples (homo sapiens). Many expression genes will be difficult to classify someone affected or not affected by Hepatocellular Carcinoma Tumor. We must take action to minimize the features without losing the data information. One of the tools to reduction dimension in Machine learning is Principal Component Analysis (PCA). Principal Component Analysis is a multivariate analysis that transforms correlated origin features into new features that do not correlate with each other by reducing the number of these features so that they have smaller dimensions but can explain most of the diversity of the original features. The objective of this research is to find the best percentage of features that have generated from PCA then fitting some models. The models that we use are Logistic Regression Classifier, Support Vector Machine (SVM) Classifier, and Random Forest Classifier. A Logistic regression model is able to provide the best accuracy starting from 40% of its variance on PCA made, which is equal to 0.875. While the Random Forest Classifier and Support Vector Machine can provide an accuracy of 0.875 when the value of the variance is above 60% of the variance. The result can give information to select the best percent in using PCA as a reduction dimension especially, for gene expression on Microarray data.

Original languageEnglish
Title of host publicationSymposium on Biomathematics 2019, SYMOMATH 2019
EditorsMochamad Apri, Vitalii Akimenko
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735420243
DOIs
Publication statusPublished - 22 Sep 2020
EventSymposium on Biomathematics 2019, SYMOMATH 2019 - Bali, Indonesia
Duration: 25 Aug 201928 Aug 2019

Publication series

NameAIP Conference Proceedings
Volume2264
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

ConferenceSymposium on Biomathematics 2019, SYMOMATH 2019
Country/TerritoryIndonesia
CityBali
Period25/08/1928/08/19

Keywords

  • Dimension reduction
  • Gene expression
  • Hepatocellular carcinoma
  • PCA
  • Support vector classifier

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