• GHANTA HEMA SANDEEPTHI
• T.GOPU
• CHUNDRU NARASIMHA MURTHY
• MALAKALAPALLI DEEPTHI
• SANKURATRI THISHITHA

Cervical cancer is a major public health concern, especially in low- and middle-income countries. Lifestyle choices to some extent have an effect on causing cervical cancer. Most cervical cancers are caused by the sexually transmitted infection caused by the Human Papillomavirus (HPV). However, only persistent HPV infections lead to progression to pre-cancer and cancer. The persistence of this infection is influenced by many factors namely, age, sexually transmitted infections, number of sexual partners, age at first sexual intercourse, number of deliveries, tobacco consumption, etc. Risk-based prediction algorithms help to stratify women with a high risk to develop cervical cancer and screen them on a priority basis. In this study, a model has been developed to predict the risk of cervical cancer based on one's lifestyle choices. This is a breakthrough study in the area of women's health that tries to enhance the predictive capability of detection of cervical cancer through machine learning combined with medical diagnosis. ML algorithms like Support Vector Machines, Random Forest, Decision Trees, XGBoost, and Logistic Regression come together to form this ensemble model. The best features of every overall prediction model are used by the ensemble model.

Machine learning, digital health, cervical cancer, human papillomavirus, risk factors, predictive modelling

"Design and Development of Machine Learning Models For Cervical Cancer Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.e728-e734, December-2024, Available :http://www.jetir.org/papers/JETIR2412482.pdf

"Design and Development of Machine Learning Models For Cervical Cancer Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. ppe728-e734, December-2024, Available at : http://www.jetir.org/papers/JETIR2412482.pdf