Random-Forest (RF) and Support Vector Machine (SVM) Implementation for Analysis of Gene Expression Data in Chronic Kidney Disease (CKD)

9th Annual Basic Science International Conference 2019

自然科学（总论）

Rustam, Zuherman^1 ; Sudarsono, Ely^1 ; Sarwinda, Devvi^1

Departement of Mathematics, Universitas Indonesia, Indonesia^1

关键词: Biological samples;    Chronic kidney disease;    Gene Expression Data;    High dimensions;    Highly accurate;    Kidney disease;    State of the art;    Transcription activity;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/546/5/052066/pdf
DOI  :  10.1088/1757-899X/546/5/052066

学科分类：自然科学（综合）

来源: IOP

【 摘 要 】

The application of mathematics in the field of bioinformatics has been widely developed. For example Support Vector Machines (SVM) and Random Forest (RF) are state of the art for classification of cancer in many applications. One of them is Chronic Kidney Disease (CKD). CKD is one of the kidney diseases that sufferers are increasing and have symptoms that are difficult to detect at first. Later, microarrays in gene expression are important tools for this approach. Microarrays gene expression provides an overview of all transcription activities in biological samples. The purpose of this research is a hybrid model combining Random Forest (RF) and Support Vector Machine (SVM) can be used to classify gene expression data. RF can highly accurate, generelize better and are interpretable and SVM (called RF-SVM) to effectively predict gene expression data with very high dimensions. In addition, from the simulation results on data from the Gene Expression Omnibus (GEO) database, it is shown that the proposed RF-SVM is a more accurate algorithm on CKD data than RFE-SVM.