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      Cervical cancer prediction using outlier deduction and over sampling methods

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      In review
      research-article
        1 , , 2
      ScienceOpen Preprints
      ScienceOpen
      Dbscan, Outlier deduction, OverSampling, Smote, Cervical cancer prediction, Machine learning
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            Abstract

            Cervical cancer is one of the disease considered to be fourth among the most common types of cancer in women around the world.

            The early deduction of cervical cancer helps to raise number of recovery patients and reduce death rates. This research work aims to

            use machine learning algorithms to predicting cervical cancer with high accuracy using an outlier deduction and over sampling

            method. Analyse the cervical cancer data available the dataset from UCI repository. In this research, first step removes outliers by

            using outlier detection method such as density-based spatial clustering of applications with noise (DBSCAN) and by increasing the

            number of cases in the dataset in a balanced way through the synthetic minority over-sampling technique (SMOTE) and SMOTE

            with Tomek link (SMOTETOmek). Finally, it employs random forest (RF) as a classifier to check the accuracy. Thus, the prediction

            model Have a two scenarios: (1) DBSCAN + SMOTETomek + RF, (2) DBSCAN + SMOTE+ RF. I found that combination of

            DBSCAN with SMOTE provided better performance than DBSCAN with SMOTETomek. Also observed that RF performed the best

            among several popular machine learning classifiers. Furthermore, the proposed research work showed better accuracy than

            previously proposed methods for forecasting cervical cancer.

            Content

            Author and article information

            Journal
            ScienceOpen Preprints
            ScienceOpen
            24 May 2022
            Affiliations
            [1 ] Department of computer science, Sri kaliswari college
            [2 ] Department of computer science, assistant professor, Sri kaliswari college
            Author notes
            Article
            10.14293/S2199-1006.1.SOR-.PPNXAUF.v1
            d7fb937b-ae0a-40f6-8fb6-2b41d4406087

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .


            The datasets generated during and/or analysed during the current study are available in the repository: http://None
            Computer science
            Dbscan,Outlier deduction,OverSampling,Smote,Cervical cancer prediction,Machine learning

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