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Health and Technology

, Volume 9, Issue 5, pp 877–886 | Cite as

Cervical cancer prognosis using genetic algorithm and adaptive boosting approach

  • Manoj SharmaEmail author
Original Paper
  • 14 Downloads

Abstract

Cervical cancer is fourth main causes of death in women. Cervix is the main origin of cervical cancer. The idea of this research is to explore and propose an efficient and improved prediction method of cervical cancer. Earlier detection and prediction methods/test were very complex, tedious and requires medical and pathological expertise. In this paper, Machine learning approach is used for prediction and detection of cervical cancer. Integrated approach of Genetic Algorithm and Adaptive Boosting is used for performance evaluation for prediction of disease. Genetic algorithm is used as attribute selector to decrease the number of attributes. This not only declines the computational cost but also reduces the number of parameters for diagnosis. Adaptive Boosting is used to improve the performance of classifiers. C 4.5 Decision Tree and Support Vector Machine (SVM) are proposed for prediction of disease. Initially 32 attributes are used for prediction of cervical cancer. The numbers of attributes are reduced with genetic algorithm and further performance enhancement is proposed with adaptive boosting technique. With proposed integrated approach of genetic algorithm and adaptive boosting the improved accuracy lies between 94.17%-94.69%, sensitivity 97.36%-98.90%, specificity 93.37%-94.72% and precision 93%-95.17% for Support Vector Machine Radial Bias Function (SVM RBF), SVM Linear and Decision Tree.

Keywords

Cervical cancer Machine learning techniques Genetic algorithm SVM RBF SVM linear Decision tree 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electronics & Communication Engineering, Giani Zail Singh Campus College of Engineering & TechnologyMRSPTUBathindaIndia

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