Prediction of Cervical Cancer Occurrence using Genetic Algorithm and Support Vector Machine

Abstract

Cervical cancer is a malignant neoplasm arising from cells originating in the cervical uteri. Cervical cancer can be treated using Human Papilloma virus vaccine and carrying out regular pap test. The manual system contains large amount of errors by virtue of human decision, the visual screening is very demanding, tedious, and expensive in terms of labor requirements. This paper proposed machine learning algorithm; Support Vector and Genetic algorithm to predict the occurrence of cervical cancer. Evaluation results show the effectiveness of the proposed approach with the overall Precision, Recall, F1 score, Sensibility, Sensitivity, Accuracy values 96%, 95%, 95%, 89%, 96%and 95% respectively for Biopsy and 97%, 96%, 96%, 50%, 97% and 96% for Hinselmann. In this study cervical cancer was predicted with Support vector machine classifier and Genetic algorithm optimization tool. The prediction was found to have acceptable performance measures which will reduce future incidence of the outbreak in the world and aid timely response of medical experts.