An Evaluation of Machine Learning Algorithms and Feature Selection Methods for Cervical Cancer Risk Prediction using Clinical Features - Khamlıchı, Sokaina El; Ouahab, Ikram Ben Abdel; Bouhorma, Mohammed; Elouaaı, Fatiha; Sedquı, Abdelfettah; Maurady, Amal (International Journal of Intelligent Systems and Applications in Engineering, 2022)
Cervical cancer is one of the most frequent gynecological cancers worldwide. It is associated to several risk factors like sexually transmitted diseases, human papillomavirus and smoking. The early diagnosis of this disease is crucial to lower fatality rates. Furthermore, its early prediction can support clinicians and patients to have an effective treatment. This study intends to compare machine learning classifiers to determine the best model to predict cervical cancer and identify its most significant risk factors. This work compares five machine learning algorithms: K-Nearest Neighbor, Gaussian Naïve Bayes, Logistic Regression, Random Forest and Decision Tree (DT). Afterwards, the study continues to enhance the outcome of DT algorithm through balancing the data with Synthetic Minority Oversampling Technique (SMOTE), selecting the most important features with Recursive Feature Elimination (RFE) and tuning hyperparameters with Grid Search technique. Overall, the combination of Decision Tree classification technique with SMOTE and tuning hyperparameters with Grid Search method presents the most performing model.