A Novel Web Framework for Cervical Cancer Detection System: A Machine Learning Breakthrough

• Published in: IEEE access Vol. 12; pp. 41542 - 41556
• Main Authors: Qathrady, Mimonah Al, Shaf, Ahmad, Ali, Tariq, Farooq, Umar, Rehman, Aqib, Alqhtani, Samar M., Alshehri, Mohammed S., Almakdi, Sultan, Irfan, Muhammad, Rahman, Saifur, Bade Eljak, Ladon Ahmed
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Artificial neural networks; Biophysics; Biopsy; Cancer; Cervical cancer; cervical cancer detection; Decision analysis; Decision trees; Detection algorithms; Diagnosis; Expert web framework; gyne cancer diagnosis; Health care; Human papillomavirus; Internet of Things; Machine learning; Medical diagnosis; Support vector machines; Web servers; Web sites
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3377124

Cervical cancer, the second most prevalent cancer among women worldwide, is primarily attributed to the human papillomavirus (HPV). Despite advances in healthcare, it remains a significant cause of mortality among women across diverse regions, surpassing other hereditary cancers. Early detection is pivotal, as survival rates exceed 90% when the disease is identified in its early stages. In response to this critical need, we introduce WFC2DS (Web Framework for Cervical Cancer Detection System), a novel expert web system specifically designed to revolutionize cervical cancer diagnosis. WFC2DS integrates a sophisticated ensemble of machine learning classification algorithms, including Artificial Neural Network (ANN), AdaBoost, K-Nearest Neighbor (KNN), Random Forest Classifier (RFC), Support Vector Machine (SVM), and Decision Tree (DT). This ensemble approach enables a comprehensive analysis of a large dataset comprising information from 858 patients with 36 attributes, with the primary objective being the early detection of cervical cancer, using the last attribute, Biopsy, as the target variable. Our evaluation criteria encompass accuracy, specificity, sensitivity, and the F1 score. Among the algorithms, RFC and DT emerge as the most promising, demonstrating exceptional performance with an accuracy of 98.1% and an F1 score of 0.98. AdaBoost shows an accuracy of 97.4% and an F1 score of 0.98, ANN attains an accuracy of 97.7% and an F1 score of 0.96, SVM achieves an accuracy of 96.2% and an F1 score of 0.96, and KNN reaches an accuracy of 90.6% with an F1 score of 0.91. This research significantly contributes to reducing the global burden of cervical cancer, emphasizing transformative advancements in women's healthcare. WFC2DS, with its cutting-edge machine learning techniques, not only improves the accuracy of cervical cancer diagnosis but also enhances the overall healthcare landscape for women worldwide.