Cervical Cancer Diagnosis Using Random Forest Classifier With SMOTE and Feature Reduction Techniques

Cervical cancer is the fourth most common malignant disease in womens worldwide. In most cases cervical cancer symptoms are not noticeable at its early stages. There are a lot of factors that increase the risk of developing cervical cancer like Human Papilloma Virus (HPV), Sexual Transmitted Diseases (STD) and smoking. Identifying those factors and building a classification model to classify whether the cases are cervical cancer or not is a challenging research. This study aims at using cervical cancer risk factors to build classification model using Random Forest (RF) classification technique with Synthetic Minority Oversampling Technique (SMOTE) and two feature reduction techniques Recursive Feature Elimination (RFE) and Principle Component Analysis (PCA). Most medical datasets are often imbalanced because the number of patients is much less than the number of non-patients. Because of the imbalance of the used dataset, SMOTE is used to solve this problem. The dataset consists of 32 risk factors and 4 target variables: Hinselmann, Schiller, Cytology and Biopsy. After comparing the results, we find that the combination of the random forest classification technique with SMOTE improve the classification performance.