Advancing Early Detection of Cervical Cancer Through Multi-Model Intelligent Classifiers and Computational Technique

One of the most common illnesses in women and one with a high fatality rate is cervical cancer. Early detection using screening techniques helps to increase the lifespan of women and lower mortality rates. According to the World Health Organization (WHO), cervical cancer is now the fourth most common malignancy to affect women. According to surveys, low- and middle-income nations account for 90% of all cancer-related deaths since there is a dearth of knowledge about the disease and its preventative treatments there. When it comes to preserving the health of women, cervical cancer research is one of the most crucial fields of study. Early detection of cervical cancer is crucial because it can save the lives of many women whose lives are at risk. Foretelling cervical cancer growth prior to administering a screening test is the goal of the proposed study, which makes use of data mining and machine learning algorithms. Feature selection methods were used to isolate the characteristics useful for diagnosing cervical cancer. When applied to the dataset, an enhanced SVM model's accuracy was found to be 94.16 percent. This highlights the model's usefulness in screening for a potentially deadly disease like cervical cancer. This type of prediction algorithm for cervical cancer is gaining popularity as a result of its high accuracy. This ensures the model's high level of predictive performance and hints that it could revolutionise the way the needy are assisted if used in real time.