Early risk prediction of cervical cancer: A machine learning approach

Abstract

Cervical cancer is a vital public health issue that affects women worldwide. As it is a fatal disease, early risk prediction of cervical cancer can play an important role in prevention by raising public awareness of this disease. Early prediction using a Machine Learning (ML) model can be a beneficial solution for both healthcare professionals and people at risk. In this study, eleven supervised ML algorithms are utilized to forecast early jeopardies of this disease using a dataset from UCI ML repository. The ML models are rummaged to prophesy the early threats, and performance parameters like accuracy, precision, F1-score, re-call, and ROC-AUC are estimated. Finally, a reasonable analysis is performed, revealing that this study achieved 93.33% prediction accuracy with Multi-Layer Perceptron (MLP) algorithm with default hyperparameters. However, employing the hyperparameter tuning method with Grid Search Cross Validation (GSCV), K-Nearest Neighbors (KNN), Decision Tree Classifier (DTC), Support Vector Machine (SVM), Random Forest Classifier (RFC), and Multi-Layer Perceptron (MLP) all portrayed accuracy of 93.33%.