Machine Learning Model for Cancer Diagnosis based on RNAseq Microarray | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 9, Volume 30, Issue 1, January 2021, Page 65-75 PDF (992.84 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2021.146277 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Hanaa Torkey1; Mostafa Atlam1; Nawal El-Fishawy1; Hanaa Salem2 | ||||
| 1dept. computer science and engineeing Faculty of Eleronic Engineering, Menoufia University Menoufia, Menouf | ||||
| 2Communications and Computers Engineering Department, Faculty of Engineering, Delta University for Science and Tecnology, Gamasa, Egypt. | ||||
| Abstract | ||||
| Microarray technology is one of the most important recent breakthroughs in experimental molecular biology. This novel technology for thousands of genes concurrently allows the supervising of expression levels in cells and has been increasingly used in cancer research to understand more of the molecular variations among tumors so that a more reliable classification becomes attainable. Machine learning techniques are loosely used to create substantial and precise classification models. In this paper, a function called Feature Reduction Classification Optimization (FeRCO) is proposed. FeRCO function uses machine learning techniques applied upon RNAseq microarray data for predicting whether the patient is diseased or not. The main purpose of FeRCO function is to define the minimum number of features using the most fitting reduction technique along with classification technique that give the highest classification accuracy. These techniques include Support Vector Machine (SVM) both linear and kernel, Decision Trees (DT), Random Forest (RF), K-Nearest Neighbours (KNN) and Naïve Bayes (NB). Principle Component Analysis (PCA) both linear and kernel, Linear Discriminant Analysis (LDA) and Factor Analysis (FA) along with different machine learning techniques were used to find a lower-dimensional subspace with better discriminatory features for better classification. The major outcomes of this research can be considered as a roadmap for interesting researchers in this field to be able to choose the most suitable machine learning algorithm whatever classification or reduction. The results show that FA and LPCA are the best reduction techniques to be used with the three datasets providing an accuracy up to 100% with TCGA and simulation datasets and accuracy up to 97.86% with WDBC datasets. LSVM is the best classification technique to be used with Linear PCA (LPCA), FA and LDA. RF is the best classification technique to be used with Kernel PCA (KPCA). | ||||
| Keywords | ||||
| Cancer Classification; Diagnosis; Gene Expression; Gene Reduction; Machine learning | ||||
| References | ||||
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