Browsing by Author "Khatun, Rabea"

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    Cancer Classification Utilizing Voting Classifier With Ensemble Feature Selection Method and Transcriptomic Data
    (MDPI Publications, 2023-09-14) Khatun, Rabea; Akter, Maksuda; Islam, Md. Manowarul; Uddin, Md. Ashraf; Talukder, Md. Alamin; Kamruzzaman, Joarder; Azad, AKM; Paul, Bikash Kumar; Almoyad, Muhammad Ali Abdulllah; Aryal, Sunil; Moni, Mohammad Ali
    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.