WITHDRAWN: H 2 O AutoML-LIME: an integrated modelling approach for predicting cervical cancer in Women

Abstract

Background Cancer positioning a major disease, particularly for middle-aged people, which remains a global concern that can be developed in the form of abnormal growth of body cells at any place in the human body. Cervical cancer, often known as cervix cancer, is cancer present at the female cervix. In the area where the endocervix (upper two-thirds of the cervix) and ectocervix (lower third of the cervix) meet, the majority of cervical cancers begin. Objective Despite an influx of people entering the healthcare industry, the demand for Machine Learning (ML) specialists has recently outpaced the supply. To close the gap, user-friendly application, such as H2O, has made significant progress these days. However, the traditional ML technique handles each stage of the process separately; whereas H2O AutoML can automate a major portion of the ML workflow, such as automatic training and tuning of multiple models within a user-defined timeframe. Methods Thus, this work aims at implementing the H2O AutoML-LIME technique, to predict cervical cancer at its early stages. Moreover, this model has capable of training the best model in less amount of time which helps in reducing the human effort over traditional ML techniques. The Stacked Ensembles approach, on the other hand, will be automatically trained H2O models to create a highly predictive ensemble model that will outperform the AutoML Leaderboard in most instances. Additionally, LIME (Local Interpretable Model-Agnostic Explanations) has been implemented over the H2O AutoML model, to uncover black boxes and to explain every individual prediction in our model. Results After predicting our proposed model with three different probabilities, gives 0.13, 0.05, and 0.13 percent of chances cervical cancer respectively.