An Advancement in a Prediction Model for Cervical Cancer Using the Cart Algorithm

Everyone, but especially women, should get regular health checks if they want to live long and happy lives. Even today, despite the fact that women have access to a lot of medical care and enjoy a high level of social standing, many of them still pass away for a variety of factors that neither they nor their loved ones are aware of. This demonstrates how little women are aware of their health's potential and regular examinations. Women are more susceptible to some of the deadliest diseases simply because they are more thoughtless and ignorant. One such terrible illness is cancer and cervix cancer. This study focuses on the data mining techniques used with biological data to accurately forecast the development of cancer cells known as malignant tumors in women's cervical tissues. Accurate results for cervical cancer are investigated using several data mining algorithms, including CART, SVM, and C4.5. The primary goal is to enhance the effectiveness of cancer cell prediction and raise the precision of the results. The obtained prediction performances are on par with those of current methods. Experiment results show that CART is the most effective algorithm for predicting the presence of Cervical Cancer cells, a novel area of research. To help researchers choose the most effective algorithm, we give predictions of the accuracy levels of a number of different algorithms based on their sensitivity, specificity, and F-Score.