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

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dc.contributor.author Khatun, Rabea
dc.contributor.author Akter, Maksuda
dc.contributor.author Islam, Md. Manowarul
dc.contributor.author Uddin, Md. Ashraf
dc.contributor.author Talukder, Md. Alamin
dc.contributor.author Kamruzzaman, Joarder
dc.contributor.author Azad, AKM
dc.contributor.author Paul, Bikash Kumar
dc.contributor.author Almoyad, Muhammad Ali Abdulllah
dc.contributor.author Aryal, Sunil
dc.contributor.author Moni, Mohammad Ali
dc.date.accessioned 2024-05-04T06:24:50Z
dc.date.available 2024-05-04T06:24:50Z
dc.date.issued 2023-09-14
dc.identifier.issn 2073-4425
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/12249
dc.description.abstract 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. en_US
dc.language.iso en_US en_US
dc.publisher MDPI Publications en_US
dc.subject Cancer detection en_US
dc.subject Machine learning en_US
dc.title Cancer Classification Utilizing Voting Classifier With Ensemble Feature Selection Method and Transcriptomic Data en_US
dc.type Article en_US


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