Improved Classification Accuracy for Identification of Cervical Cancer

Authors

  • D. Merlin  Department of Computer Science, Bishop Heber College (Autonomous), Affiliated to Bharathidasan University, Tiruchirappalli, India
  • Dr. J. G. R. Sathiaseelan  Head, Department of Computer Science, Bishop Heber College (Autonomous), Affiliated to Bharathidasan University, Tiruchirappalli, India

DOI:

https://doi.org/10.32628/CSEIT217633

Keywords:

Decision Tree, Naive Bayes, KNN, SVM, MLP

Abstract

The major purpose of this research is to forecast cervical cancer, compare which algorithms perform well, and then choose the best algorithm to predict cervical cancer at an early stage. Cervical cancer classification can be automated using a machine learning system. This study evaluates multiple machine learning techniques for cervical cancer classification. For this classification, algorithms such as Decision Tree, Naive Bayes, KNN, SVM, and MLP are proposed and evaluated. The cervical cancer Dataset, which was retrieved from the UCI machine learning data repository, was used to test these methods. With the help of Sciklit-learn, the algorithms' results were compared in terms of Accuracy, Sensitivity, and Specificity. Sciklit-learn is a Python-based machine learning package that is available for free. Finally, the best model for predicting cervical cancer is developed.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-12-30

Issue

Volume 7, Issue 6, November-December-2021

Section

Research Articles

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How to Cite

[1]
D. Merlin, Dr. J. G. R. Sathiaseelan, " Improved Classification Accuracy for Identification of Cervical Cancer" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 6, pp.245-258, November-December-2021. Available at doi : https://doi.org/10.32628/CSEIT217633