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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.

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Healthcare related data are very important for people of the global community to make them aware of their lives and rights. By analyzing performances of different types of health-related data produce efficient valuable rich information; which can be used in further healthcare research. From the previous works we have seen, they have also tried to analyze the performances of algorithms to modify the accuracy but this study is unique as it has used lots of different types of health-related datasets and algorithms together to achieve better accuracy. This study aims to analyze performances of algorithms on different types of healthcare related data to produce effective information in order to assist global community in their daily life. This will help people to lead a healthy and comfortable life and innovate new ideas to further change their lifestyle. The effective information or the standard used here is the accuracy of the applied algorithms.