An Effective Method for Predicting Cervical Cancer Risk Using Semantic-Preserved Generative Adversarial Networks to Enhance Prediction Accuracy and Model Efficiency

Cervical cancer is a main public health concern, particularly in low-and middle-income countries. A certain amount of cervical cancer is cause by lifestyle choices. The Human Papillomavirus (HPV) is the sexual transmitted infection that causes the most cervical cancers. But only persistent HPV infections cause pre-cancer and cancer to proceed. Risk-dependent prediction approach aid to stratify female with higher risk to developing cervical cancer with screened them on priority basis. An Effective Method for Predicting Cervical Cancer Risk Using Semantic-Preserved Generative Adversarial Networks to Enhance Prediction Accuracy and Model Efficiency (PCC-SPGAN-PAM) is proposed. Initially, data are collected from UCI dataset. Then collected data is fed in to preprocessing phase. Trusted Based Distributed Set Membership Filtering (TDSF) is used for data cleaning and missing value is restored for target tracking. Then, the pre-processed data are fed into Quaternion Offset Linear Canonical Transform (QOLCT) to extract the features including number of sexual partners, age, deliveries, sexually transmitted infections, tobacco usage, age at first sexual intercourse. The extracted features are fed in to SPGAN for predicted risk of cervical cancer as positive, negative. PCC-SPGAN-PAM has developed to detect risk of cervical cancer dependent on one’s lifestyle choice. The PCC-SPGAN-PAM is selected to arrive at accuracy of 99.23%. It is effectual to relate risk factors and PCC that aid ineffectual prevention with management of cervical cancer. The proposed technique is implemented in Python. The performance metrics is analyzed and the results are compared with existing techniques: Design with Development of Efficient Risk Prediction Method for Cervical Cancer (ERP-CC), cervical cancer prediction utilizing stacked ensemble approach along SMOTE-RFERF (CCP-SEA), ensemble classification method utilizing behavioural risk factors for cervical cancer prediction (EC-CCP-BRF) techniques respectively.