Comparative Study of Statistical Techniques for Preliminary Diagnosis of Cancer Risk

Abstract

The purpose of the is to elaborate models for preliminary diagnosis using statistical techniques. We compare two models for the estimation of cervical cancer risks.This article aims to compare predictive models for cervical cancer using machine learning techniques. We set up classification tables to compare the overall correct prediction rates.The data used comes from 30 cases of cervical cancer. We fitted a Logistic Regression (LR) model and trained Artificial Neural Networks (ANNs). The multicollinearity problem, usually present in modeling with numerous predictive variables, was addressed with factor analysis and Pearson Correlations.The LR model and ANN model were evaluated based on their percentage of correct classifications. The LR model achieved an accuracy of 33.33%, while the ANN model achieved an accuracy of 16.67%.Based on the percentage of correct classification, the Logistic Regression model was superior to the Neural Networks for the cervical cancer dataset. This highlights the need for further exploration of different machine learning approaches and data preprocessing techniques to improve predictive performance for cervical cancer risks.