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Cancer Classification Utilizing Voting Classifier with Ensemble Feature Selection Method and Transcriptomic Data

journal contribution
posted on 2023-10-06, 02:19 authored by Rabea Khatun, Maksuda Akter, Md Manowarul Islam, Md Ashraf Uddin, Md Alamin Talukder, Joarder Kamruzzaman, AKM Azad, Bikash Kumar Paul, Muhammad Ali Abdulllah Almoyad, Sunil AryalSunil Aryal, Mohammad Ali Moni
Biomarker-based cancer identification and classification tools are widely used in bioinformatics and machine learning fields. However, the high dimensionality of microarray gene expression data poses a challenge for identifying important genes in cancer diagnosis. Many feature selection algorithms optimize cancer diagnosis by selecting optimal features. This article proposes an ensemble rank-based feature selection method (EFSM) and an ensemble weighted average voting classifier (VT) to overcome this challenge. The EFSM uses a ranking method that aggregates features from individual selection methods to efficiently discover the most relevant and useful features. The VT combines support vector machine, k-nearest neighbor, and decision tree algorithms to create an ensemble model. The proposed method was tested on three benchmark datasets and compared to existing built-in ensemble models. The results show that our model achieved higher accuracy, with 100% for leukaemia, 94.74% for colon cancer, and 94.34% for the 11-tumor dataset. This study concludes by identifying a subset of the most important cancer-causing genes and demonstrating their significance compared to the original data. The proposed approach surpasses existing strategies in accuracy and stability, significantly impacting the development of ML-based gene analysis. It detects vital genes with higher precision and stability than other existing methods.

History

Journal

Genes

Volume

14

Article number

1802

Pagination

1-24

Location

Basel, Switzerland

ISSN

2073-4425

eISSN

2073-4425

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Issue

9

Publisher

MDPI