Prediction of Cervical Cancer using Voting and DNN Classifiers

Cervical tumor is one of the far-reaching sicknesses among ladies in India and around the world. Early analysis is good for better treatment, yet due to vulnerability in detecting cancer cells becoming complex. Machine Learning (ML) systems can be used to create tools for doctors in the conclusion of cervical growth, which will enormously upgrade the survival rate of patients. In this work, two popular ML techniques like Voting Classifier and Deep Neural Network (DNN) Classifier are used to predict cervical growth. The cervical cancer (risk factors) datasets from UCI store was utilized as a preparation set to assess and look at the execution of the two ML classifiers in terms of accuracy and other false rate measures. Outcomes acquired in this paper give how effectively ML systems predict cervical malignancy.