Predicting Recurrence in Cervical Cancer Patients Using Clinical Feature Analysis

The paper demonstrates an analytic approach for prediction of recurrence in the cervical cancer patients using a probabilistic model. The techniques used for classification and prediction are based on recognizing typical and diagnostically most important test features relating to cervical cancer. The main contributions of the research involve predicting the probability of recurrences in no recurrence (First time detection) cases. The conventional statistical and machine learning tools are applied for the analysis. The experimental study demonstrates the feasibility and promising the proposed approach for the said cause with real data.