Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods

Abstract

Globally, cervical cancer remains as the foremost prevailing cancer in females. Hence, it is necessary to distinguish the importance of risk factors of cervical cancer to classify potential patients. The present work proposes a cervical cancer prediction model (CCPM) that offers early prediction of cervical cancer using risk factors as inputs. The CCPM first removes outliers by using outlier detection methods such as density-based spatial clustering of applications with noise (DBSCAN) and isolation forest (iForest) and by increasing the number of cases in the dataset in a balanced way, for example, through synthetic minority over-sampling technique (SMOTE) and SMOTE with Tomek link (SMOTETomek). Finally, it employs random forest (RF) as a classifier. Thus, CCPM lies on four scenarios: (1) DBSCAN + SMOTETomek + RF, (2) DBSCAN + SMOTE+ RF, (3) iForest + SMOTETomek + RF, and (4) iForest + SMOTE + RF. A dataset of 858 potential patients was used to validate the performance of the proposed method. We found that combinations of iForest with SMOTE and iForest with SMOTETomek provided better performances than those of DBSCAN with SMOTE and DBSCAN with SMOTETomek. We also observed that RF performed the best among several popular machine learning classifiers. Furthermore, the proposed CCPM showed better accuracy than previously proposed methods for forecasting cervical cancer. In addition, a mobile application that can collect cervical cancer risk factors data and provides results from CCPM is developed for instant and proper action at the initial stage of cervical cancer.

Integrating Enhanced Sparse Autoencoder-Based Artificial Neural Network Technique and Softmax Regression for Medical Diagnosis

“…In Table 5, we compare the proposed approach with some recent scholarly works that used the cervical cancer dataset, including principal component analysis (PCA)-based SVM [33], a research work where the dataset was preprocessed and classified using numerous algorithms, in which LR and SVM had the best accuracy [34], a C5.0 decision tree [35]. The other methods include a multistage classification process which combined isolation forest (iForest), synthetic minority over-sampling technique (SMOTE), and RF [36], a sparse autoencoder feature learning method combined ANN classifier [12], and a feature selection method combined with C5.0 and RF [37]. [35] C5.0 96 Ijaz et al [36] iForest+SMOTE+RF 98.925 Mienye et al [12] SAE+ANN 98…”

“…The other methods include a multistage classification process which combined isolation forest (iForest), synthetic minority over-sampling technique (SMOTE), and RF [36], a sparse autoencoder feature learning method combined ANN classifier [12], and a feature selection method combined with C5.0 and RF [37]. [35] C5.0 96 Ijaz et al [36] iForest+SMOTE+RF 98.925 Mienye et al [12] SAE+ANN 98…”

TADILOF: Time Aware Density-Based Incremental Local Outlier Detection in Data Streams

“…In [ 14 ], the authors provide survey of unsupervised machine learning algorithms that are proposed for outlier detection. In [ 15 ], the authors propose a cervical cancer prediction model (CCPM) for early prediction of cervical cancer using risk factors as inputs. The authors utilize several machine learning approaches and outlier detection for different preprocessing tasks.…”

A New Under-Sampling Method to Face Class Overlap and Imbalance

“…It deals with the noisy instances in the majority class via a noise filter based on the DBSCAN clustering algorithm [30] combined with the use of a minimum spanning tree (MST) algorithm to reduce the size of the negative class. The reason to combine the DBSCAN clustering with the MST approach is because DBSCAN has demonstrated to be a powerful tool for identifying and removing noisy instances and cleaning the overlapping between classes [31], but it does not produce a well-balanced class distribution. By viewing the data set as a weighted complete graph, the MST algorithm allows for discovering the core of the majority class, which is further used to remove the amount of redundant negative instances needed to balance both classes.…”