LEVERAGING MACHINE LEARNING FOR EARLY DETECTION OF CERVICAL CANCER: ANALYZING DEMOGRAPHIC, CLINICAL, AND LIFESTYLE RISK FACTORS

Cervical cancer remains a significant global health challenge, particularly in developing countries like India, where early detection plays a crucial role in reducing mortality rates. This study aims to investigate the utility of machine learning models in the early-stage detection of cervical cancer using demographic, clinical, and image data. We used different existing machine learning algorithms to get correlations between risk factors such as age, smoking status, HPV infection, contraceptive use, and the number of sexual partners with the likelihood of cervical cancer development. Our findings state that the potential of machine learning-based models in improving early detection is highly significant. The proposed approach recommends a promising avenue for integrating machine learning into clinical practice to enhance cervical cancer screening and improve patient outcomes.