Cervical cancer prognosis using genetic algorithm and adaptive boosting approach

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.