Cervical Cancer Diagnosis Model Using Extreme Gradient Boosting and Bioinspired Firefly Optimization

Cervical cancer is frequently a deadly disease, common in females. However, early diagnosis of cervical cancer can reduce the mortality rate and other associated complications. Cervical cancer risk factors can aid the early diagnosis. For better diagnosis accuracy, we proposed a study for early diagnosis of cervical cancer using reduced risk feature set and three ensemble-based classification techniques, i.e., extreme Gradient Boosting (XGBoost), AdaBoost, and Random Forest (RF) along with Firefly algorithm for optimization. Synthetic Minority Oversampling Technique (SMOTE) data sampling technique was used to alleviate the data imbalance problem. Cervical cancer Risk Factors data set, containing 32 risks factor and four targets (Hinselmann, Schiller, Cytology, and Biopsy), is used in the study. The four targets are the widely used diagnosis test for cervical cancer. The effectiveness of the proposed study is evaluated in terms of accuracy, sensitivity, specificity, positive predictive accuracy (PPA), and negative predictive accuracy (NPA). Moreover, Firefly features selection technique was used to achieve better results with the reduced number of features. Experimental results reveal the significance of the proposed model and achieved the highest outcome for Hinselmann test when compared with other three diagnostic tests. Furthermore, the reduction in the number of features has enhanced the outcomes. Additionally, the performance of the proposed models is noticeable in terms of accuracy when compared with other benchmark studies for cervical cancer diagnosis using reduced risk factors data set.