A comparison of machine learning algorithms and imbalance data techniques to predict cervical cancer (Preprint)
Authors: Ortiz Sr Christian Alfonso 
Published: May 2022

BACKGROUND

Cervical cancer is a leading cause of death in women. The present research analyzes, explores, compares and identifies the best method for predicting cervical cancer by applying machine learning techniques. The dataset used is from the University Hospital of Caracas, Venezuela where a selection of variables was made according to the literature in order to predict cervical cancer. Seven algorithms were applied: Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), XGBoost (XG), Naive Bayes (NB), Multi-layer Perceptron (MLP) and K-Nearest Neighbors (KNN). Furthermore, three data balancing techniques were applied: SMOTETomek, SMOTE, and ROS for Hinselmann, Schiller, Cytology and Biopsy as target variables. In addition, accuracy, precision, recall, f-score and AUC were used to evaluate the results. The algorithm that stood out best in accuracy, precision and f-score was Random Forest with 94.57%, 72.46% and 60.70% respectively. For recall Logistic Regression stands out with 68.37%, and for AUC, Naive Bayes stands out with 79.11%.

OBJECTIVE

The present research analyzes, explores, compares and identifies the best method for predicting cervical cancer by applying machine learning techniques.

METHODS

A. Dataset description
B. Data preprocessing
C. Variable selection
D. Balancing data
E. Algorithms
F. Evaluation

RESULTS

Section is divided into four parts. The first three points show the results of the evaluation metrics for the prediction algorithms classified by the data balancing techniques already mentioned in the previous section.
The last section shows the experiments performed for the Naive Bayes algorithm with an imbalanced dataset.

CONCLUSIONS

If we consider the technique used in this work of variable selection based on the literature, the highest accuracy was obtained with Random Forest. This result agrees with the work of [21] where the highest accuracy is obtained with Random Forest. In addition, the work of [19] also obtains high accuracy values for Random Forest considering Hinselmann as the target variable.
It can be assumed that for the selection of variables the method that best stands out from the other methods, obtaining the best results in all its metrics and for all scenarios is Random Forest, where SMOTETomek and SMOTE stand out for the Hinselmann variable with an accuracy of 94.57%, this being the highest value.
The AUC value is used to compare the performance of the models, in the present research highlights the Hinselmann variable together with Naive Bayes giving the best values with SMOTE, ROS and SMOTETomek as can be seen in Table 5, in the same way the Hinselmann variable is highlighted with the imbalanced dataset as found in Table 8. It can be assumed that the AUC obtains better results for the selection of variables against the imbalanced dataset with a maximum value of 79.11% and 76.58% respectively.
The XGBoost algorithm with SMOTE and Citology obtains the lowest result for AUC with 31.74%, being one of the algorithms with the best accuracy of 93.02%, this is a clear difference that this algorithm cannot identify or classify in an adequate way the positive classes from the negative classes since an algorithm to be efficient must have an AUC higher than 70%.