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Performance analysis of machine learning based optimized feature selection approaches for breast cancer diagnosis

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Abstract

Healthcare systems around the world are facing huge challenges in responding to trends of the rise of chronic diseases. The objective of our research study is the adaptation of Data Science and its approaches for prediction of various diseases in early stages. In this study we review latest proposed approaches with few limitations and their possible solutions for future work. This study also shows importance of finding significant features that improves results proposed by existing methodologies. This work aimed to build classification models such as Naïve Bayes, Logistic Regression, k-Nearest neighbor, Support vector machine, Decision tree, Random Forest, Artificial neural network, Adaboost, XGBoost and Gradient boosting. The experimental study chooses group of features by means of three feature selection approaches such as Correlation-based selection, Information Gain based selection and Sequential feature selection. Various Machine learning classifiers are applied on these feature subsets and based on their performance best feature subset is selected. Finally, ensemble based Max Voting Classifier is proposed on top of three best performing models. The proposed model produces an enhanced performance label with accuracy score of 99.41%.

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Acknowledgements

The authors are highly thankful to the editor and reviewers for kind suggestions and critical comments for improving the quality of the paper.

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Authors and Affiliations

  1. Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, 221005, India

    Ajay Sharma & Pramod Kumar Mishra

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  1. Ajay Sharma
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  2. Pramod Kumar Mishra
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Correspondence to Ajay Sharma.

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Sharma, A., Mishra, P.K. Performance analysis of machine learning based optimized feature selection approaches for breast cancer diagnosis. Int. j. inf. tecnol. 14, 1949–1960 (2022). https://doi.org/10.1007/s41870-021-00671-5

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  • DOI: https://doi.org/10.1007/s41870-021-00671-5

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