Identification of Most Relevant Breast Cancer miRNA using Machine Learning Algorithms

Mehejabin, Tasnime; Rahman, Faria; Yeasmin, Soniya; Sarkar, Manika

doi:10.1109/icccnt49239.2020.9225624

2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) 2020

DOI: 10.1109/icccnt49239.2020.9225624

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Identification of Most Relevant Breast Cancer miRNA using Machine Learning Algorithms

Tasnime Mehejabin¹,

Faria Rahman²,

Soniya Yeasmin³

et al.

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2021

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Research Methods in Machine Learning: A Content Analysis

Kamiri

Mariga

2021

IJCIT

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Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research. To achieve this the researchers analyzed a total of 100 articles published since 2019 in IEEE journals. This study revealed that Machine learning uses quantitative research methods with experimental research design being the de facto research approach. The study also revealed that researchers nowadays use more than one algorithm to address a problem. Optimal feature selection has also emerged to be a key thing that researchers are using to optimize the performance of Machine learning algorithms. Confusion matrix and its derivatives are still the main ways used to evaluate the performance of algorithms, although researchers are now also considering the processing time taken by an algorithm to execute. Python programming languages together with its libraries are the most used tools in creating, training, and testing models. The most used algorithms in addressing both classification and prediction problems are; Naïve Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree. The recurring themes identified in this study are likely to open new frontiers in Machine learning research.

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Research Methods in Machine Learning: A Content Analysis

Kamiri

Mariga

2021

IJCIT

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scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Identification of Most Relevant Breast Cancer miRNA using Machine Learning Algorithms

Cited by 1 publication

References 8 publications

Research Methods in Machine Learning: A Content Analysis

Research Methods in Machine Learning: A Content Analysis

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