Automated invasive cervical cancer disease detection at early stage through suitable machine learning model

Abstract

Cervical cancer is a common cancer that affects women all over the world. This is the fourth leading cause of death among women and has no symptoms in its early stages. At the cervix, cervical cancer cells develop slowly. If it can be detected early, this cancer can be successfully treated. Health professionals are now facing a major challenge in detecting such cancer until it spreads rapidly. This study applied various machine learning classification methods to predict cervical cancer using risk factors. The main aim of this research work is to be described of the performance variation of eight most classifications algorithm to detect cervical cancer disease based on the selection of various top features sets from the dataset. Multilayer Perceptron (MLP), Random Forest and k-Nearest Neighbor, Decision Tree, Logistic Regression, SVC, Gradient Boosting, AdaBoost are examples of machine learning classification algorithms that have been used to predict cervical cancer and help in early diagnosis. A variety of approaches are used to avoid missing values in the dataset. To choose the various best features, a combination of feature selection techniques such as Chi-square, SelectBest and Random Forest was used. The performance of those classifications is evaluated using the accuracy, recall, precision and f1-score parameters. On a variety of top feature sets, MLP outperformed other classification models. The majority of classification models, on the other hand, claim to have the highest accuracy on the top 25 features in dataset splitting ratio (70:30). For each model, the percentage of correctly classified instances has been presented and all of the results are then discussed. Medical professionals will be able to use the suggested approach to perform research on cervical cancer.

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

“…Multivariate adjustment and multiple-regression techniques were introduced for prediction (that is, for estimating the predicted value of a certain outcome as a function of given values of independent variables) [ 82 ]. AI studies using machine learning principles have focused on algorithms to predict cervical cancer [ 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 ]. The most important predictors of cervical cancer were age, age at first sexual intercourse, number of sexual partners, pregnancies, smoking, period of smoking (years), hormonal contraceptives, period of use of hormonal contraceptives (years), IUD, period of use of IUD (years), STDs, period of STDs (years), Schiller, Hinselmann, cytology, the presence of 15 high-risk HPV genotypes [ 55 , 56 , 57 , 58 , 60 , 84 ], social status, marital status, personal health level, education level, and the number of caesarean deliveries [ 63 ].…”

Predicting the Spread of Vessels in Initial Stage Cervical Cancer through Radiomics Strategy Based on Deep Learning Approach

“…Medical practitioners can carry out cervical cancer prediction in an effective manner by using the recommended method. is method has a cumulative loss function, making it difficult to predict cancer [22].…”

Optimised feature selection and cervical cancer prediction using Machine learning classification

“…6,7 Recently, diseases have been classified using computer vision, machine learning (ML) and deep learning (DL) algorithms. [8][9][10] The various ML classifiers, including Logistic Regression (LR), Decision Tree (DT), Artificial Neural Networks (ANN), Support Vector Machine (SVM) and Naive Bayes (NB), along with feature optimisation methods such as Chicken Swarm optimisation can be used for prediction. [11][12][13][14] The present study used ML methods to predict the outcome of various methods used for diagnosing cervical cancer.…”