Applying Machine Learning Algorithms to Investigate Cervical Cancer

Cervical cancer is a significant health challenge globally, particularly in regions with limited access to healthcare resources. Timely and accurate classification of cervical cancer risk is crucial for effective interventions and personalized treatment plans. The paper provides an overview of preliminary results in cervical cancer risk classification, with a focus on three machine learning algorithms namely Support Vector Machines, Capsule CNN and CNN. Support Vector Machine (SVM), Capsule Network, and Convolution Neural Network have emerged as promising methods to predict cervical cancer risk based on various risk factors and biomarkers. These algorithms analyze the available datasets on Cervical Cancer from Kaggle and identify complex patterns, demonstrating potential risk in improving cancer detection accuracy. In addition, we emphasize the importance of prevention measures, including HPV vaccination and regular screenings, to combat cervical cancer effectively. By combining innovative classification algorithms with preventive efforts, we can enhance cervical cancer management and improve patient outcomes worldwide.