Investigating Cervical Cancer Detection Frameworks Based on Machine Learning: The Critical Tradeoff Between Accuracy and Data Security

Cervical cancer has emerged as the most prevalent and deadly illness affecting women across the globe. Researchers are trying their best to detect this life-threatening illness accurately. In view of this only, machine learning approaches, multiple medical procedures, statistical models, etc., have been utilized to provide optimized and efficient treatment to all patients to protect their lives. In this study, we have compared previously proposed frameworks for the early detection of cervical cancer and analysis of patients’ data security. We demonstrated the respective benefits and limitations, investigated the datasets and the type of data employed, and analyzed the accuracy of the healthcare procedures utilized for patients in terms of improving management. The limitations of reviewed studies show that more reliable proposals need to be presented by researchers in future. Based on this only, it is concluded that the accurate and early detection of cervical cancer shows a tradeoff with patients’ data security while communicating across healthcare institutions.