Cervical Cancer Prediction Using Machine Learning

Abstract

Cervical cancer constitutes a profound global health challenge, affecting a substantial number of women annually. If this cancer is not diagnosed and attended with hypervigilance, it can spread to other body parts, damage tissues, and typically deteriorate the immune system of the body. This ultimately becomes deadly and, in many cases, non-curable. This research paper looks into machine learning used to predict cervical cancer. The 5-year relative survival rate after the spread of the disease is almost 50%. Hence, detecting the tumor in advance can prevent its proliferation and consequently assist in the process of curing the disease. 
 The study emphasizes the global impact of cervical cancer, shedding light on its prevalence and its connection to the human papillomavirus (HPV). In a managerial but approachable manner, this research discusses the factors like weakened immune systems, smoking, and contraceptive use that contribute to the risk of cervical cancer. Four essential diagnostic tests—Hinselmann, Schiller, Citology, and Biopsy—are discussed as integral components of the predictive model. Diverse Machine Learning algorithms used show promise in enhancing accuracy for early cervical cancer prediction. With potential implications for public health, this paper concludes by providing insights into future research directions.