Performance Evaluation of Supervised Learning Classifiers in Cervical Cancer Risk Stratification

Deepa S, Priya V

Abstract


Cervical cancer is a most important global health issue that affects a large number of women each year. It is one of the main causes of death in women around the world. Detecting cervical cancer earlier and accurately predicting the risk can greatly increase the chances of successful treatment and help save lives. This study aims to create a classification model using data mining techniques to identify people who are at high risk of developing cervical cancer. This enables timely medical action and treatment. The study uses data mining algorithms to determine which patients are at high risk of cervical cancer and whether they need a biopsy. After applying data preprocessing methods, several classification algorithms such as Naïve Bayes (NB), Instance-Based learner with parameter k (IBK), AdaBoost1, Random Forest (RF) and J48 were trained and tested. The performance of these models was evaluated using various classification measures like precision, recall and F-Measure. Among all the models, Random Forest showed the highest accuracy of 98.75% and it outperforms other algorithms.

Keywords


Cervical cancer, Naïve Bayes, IBK, Random Forest, J48, AdaBoost

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