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.
Highlights
Invasive happens in the woman’s cervix by affecting the deeper tissues of the cervix
This research selects a variety of different top features using a combination of feature selection techniques, reducing training time and assisting oncologists in quickly detecting cervical cancer, and the main objectives of this research are to improve classification performance using machine learning classification techniques and provide the performance results analysis based on different top features set
The proposed method is evaluated on a system with 8 Gradient boosting (GB) of RAM and a 3.0 GHz Intel Core i-7 processor using the cloud-based web application environment named Google Colab was used to create the model and use classification algorithms to detect cervical cancer disease on the dataset
Summary
Invasive happens in the woman’s cervix by affecting the deeper tissues of the cervix. The cervical cancer can spread to other parts of their body lungs, liver, bladder, vagina and rectum. Healthy cells in the cervix develop changes (mutations) growing, multiplying at a set rate and eventually dying at a set time in their DNA. Abnormal mutational activities of unhealthy cell are growing, multiplying cells out of control as well as they do not die Technology, Dhaka, Bangladesh. The most well-informed symptoms of cervical cancer unusual pain after sex, vaginal bleeding after sex, between periods, after menopause or after a pelvic examination as well as vaginal discharge. The most common diverse risk factors are many sexual partners, early sexual activity, sexually transmitted infections (STIs), weakened immune system, smoking, exposure to miscarriage prevention drugs and the like [1]
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