A Comparative Study on Machine Learning Classifiers for Cervical Cancer Prediction: A Predictive Analytic Approach

Abstract

INTRODUCTION: Cervical cancer is a significant global health concern, particularly in underdeveloped nations where preventive healthcare measures are limited. Early identification of the risks associated with cervical cancer is essential for both prevention and treatment. OBJECTIVES: In recent years, machine-learning algorithms have gained popularity as potential techniques for determining a person's risk of developing cancer based on demographic and medical information. This study uses a dataset that contains patient demographics, clinical history, and results from diagnostic tests to examine how machine learning-based algorithms can be used to predict the risks of cervical cancer. METHODS: Various machine learning approaches are used to create predictive systems, including Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Logistic Regression (LR), Gradient Boosting (GB), Nearest Centroid (NC), Multilayer Perceptron(MP), and AdaBoost (AB). RESULTS: The prediction capability of these models is assessed using performance metrics such as accuracy, sensitivity, specificity, f-measure, precision, and area under the receiver operating characteristic curve (AUC-ROC). Our results show that the decision tree has the highest accuracy, precision, and f1-score (98.91%, 97.81%, and 0.9889). Additionally, model performance was optimized by the use of hyperparameter tuning. After hyperparameter adjustment, the Support Vector Machine (SVM) showed superior accuracy of 99.64%, precision of 99.26%, and an F1-score of 0.9963, thereby indicating its potential in cervical cancer probability prediction. We also created a web application that uses a machine-learning model to estimate the risk of cervical cancer. CONCLUSION: The findings of this study highlight the significance of SVM and demonstrate the potential and capabilities of machine learning techniques to enhance accurate prediction and patient outcomes for cervical cancer screening.