Cervical Cancer Diagnostics Using Machine Learning Algorithms and Class Balancing Techniques

Abstract

Objectives:Cervical cancer is present in most cases of squamous cell carcinoma. In most cases, it is the result of an infection with human papillomavirus or adenocarcinoma. This type of cancer is the third most common cancer of the female reproductive organs. The risk groups for cervical cancer are mostly younger women who frequently change partners, have early sexual intercourse, are infected with human papillomavirus (HPV), and who are nicotine addicts. In most cases, the cancer is asymptomatic until it has progressed to the later stages. Cervical cancer screening rates are low, especially in developing countries and in some minority groups. Due to these facts, the introduction of a tentative cervical cancer screening based on a questionnaire can enable more diagnoses of cervical cancer in the initial stages of the disease. Methods: In this research, publicly available cervical cancer data collected on 859 female patients are used. Each sample consists of 36 input attributes and four different outputs Hinselmann, Schiller, cytology, and biopsy. Due to the significant unbalance of the data set, class balancing techniques were used, and these are the Synthetic Minority Oversampling Technique, the ADAptive SYNthetic algorithm (ADASYN), SMOTEEN, random oversampling, and SMOTETOMEK. To obtain the mentioned target outputs, multiple artificial intelligence (AI) and machine learning (ML) methods are proposed. In this research, multiple classification algorithms such as logistic regression, multilayer perceptron (MLP), support vector machine (SVM), K-nearest neighbors (KNN), and several naive Bayes methods were used. Results: From the achieved results, it can be seen that the highest performances were achieved if MLP and KNN are used in combination with Random oversampling, SMOTEEN, and SMOTETOMEK. Such an approach has resulted in mean area under the receiver operating characteristic curve (AUC¯) and mean Matthew’s correlation coefficient (MCC¯) scores of higher than 0.95, regardless of which diagnostic method was used for output vector construction. Conclusions: According to the presented results, it can be concluded that there is a possibility for the utilization of artificial intelligence (AI) and machine learning (ML) techniques for the development of a tentative cervical cancer screening method, which is based on a questionnaire and an AI-based algorithm. Furthermore, it can be concluded that by using class balancing techniques, a certain performance boost can be achieved.

A Systematic Review on IoT and Machine Learning Algorithms in E-Healthcare

“…Learning from labeled data, they categorize new patient cases, automating the process for improved efficiency, reduced errors, and prompt healthcare interventions [9]. Algorithms like SVM, KNN, and RF were utilized by incorporating class-balancing techniques for enhanced performance in cervical cancer diagnosis [10]. Various machine learning algorithms were applied to assess cardiovascular disease risk in people over 50 years discussed in [11].…”

“…In Reference [24], the authors proposed a more accurate Multi Voter Multi Commission Nearest Neighbor (MVMCNN) model for diabetes prediction. KNN has been used as a classification algorithm for classifying diseases such as diabetes [24], cervical cancer [10], heart disease [25] [26], and blood pressure [25].…”

Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and Machine Learning Approaches

“…The evolution of AI, particularly with the integration of ML, has opened new possibilities in oncology, revolutionizing every facet of cancer care. It has significantly enhanced cancer diagnosis [90][91][92][93], prognosis [94][95][96][97][98], and the prediction of metastasis [99][100][101].…”

“…The evolution of AI, particularly with the integration of ML, has opened new possibilities in oncology, revolutionizing every facet of cancer care. It has significantly enhanced cancer diagnosis [90][91][92][93], prognosis [94][95][96][97][98], and the prediction of metastasis [99][100][101]. Treatment selection [102], efficacy [103][104][105], response assessment [106][107][108][109][110], and outcome prediction [111][112][113][114][115] have also seen remarkable enhancements.…”

Cervical Transformation Zone Segmentation and Classification based on Improved Inception-ResNet-V2 Using Colposcopy Images

“…According to the literature, only cervical intra-epithelial neoplasia (CIN) classification is performed utilizing colposcopy images. Recently many works have been reported the utilization of deep learning in cervical cancer screening, [21][22][23] breast cancer [24][25][26] and lung cancer. 27 There has been no work on the TZ classification to date.…”