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Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required.

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Cervical cancer is one of the most common cancers among women in the world. As at the earlier stage, cervical cancer has fewer symptoms. Cancer research is vital as the prognosis of cancer enables clinical applications for patients. In this study, we demonstrate a new approach that applies an ensemble approach to machine learning models for the automatic diagnosis of cervical cancer. The dataset used in the study is the cervical cancer dataset available at the University of California Irvine database repository. Initially, missing values are imputed (k-nearest neighbors) and then the data are balanced (oversampled). Two feature selection approaches are used to extract the most significant features. The proposed stacking architecture, applied for the first time on the cervical cancer dataset, used time elapse of 5.6 s and achieved an area under the curve score of 99.7% performing better than the methods used in previous works. The objective of the study is to propose a computational model that can predict the diagnosis of cervical cancer efficiently. Further, the proposed learning architecture is gauged with several ensemble approaches like random forest, gradient boosting, voting ensemble and weighted voting ensemble to perceive the enhancement.

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