Artificial Intelligence based Cervical Cancer Risk Prediction Using M1 Algorithms

Abstract

Cervical cancer growth is the fourth maximum of regular diseases in females. It is brought about by long haul disease in skin cells and mucous film cells of the genital region. The World Health Organization (WHO) considers malignant growth a nonexclusive term for a huge gathering of infections that can influence any piece of the body, which is profoundly risky. In 2018, an expected 5,70,000 females were determined to have cervical malignancy worldwide, and around 3,11,000 females passed on from the illness. Hence proposing a model with high precision and high accuracy for diagnosing at the right phase of contamination will help a lot. This paper aims to develop machine learning(ML) algorithms like Support Vector Machine(SVM), Random Forest(RF) and Deep Learning (DL)models like Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN) using python, which gives more accurate results compared to existing models. The accuracy of each model SVM, CNN, RF and ANN obtained was 97%, 95.3%, 94% and 9 5.2%, respectively, where SVM has higher precision among ML algorithms similarly, CNN has the highest precision among the neural network algorithms, So to anticipate the cervical disease and to help in its initial judgments which can shield women in huge scope from being affected to this disease.