Enhancing Cervical Cancer Diagnosis through Advanced Feature Engineering and Machine Learning Techniques

Abstract

Cervical cancer continues to be a considerable global health problem, with a burden disproportionately in the developing world, where it ranks as the fourth most common cancer among women. Although this disease is preventable with timely diagnosis and treatment, current modalities available for screening such as Pap smear and HPV testing are limited due to barriers in accessibility, cost, and variability in interpretation of results. This requires building up more powerful diagnostic tools. In recent years, machine learning (ML) and the feature engineering process, have opened the possibility of improving cervical cancer diagnosis by evaluating very deep patterns hidden in medical data and not perceived by human analysis. Also, different machine learning models like Logistic Regression, Random Forest, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Decision Trees are the best for diagnostic purpose accuracy, precision, recall and F1-score after applying the feature selection techniques. Specifically, the findings highlight the value of more sophisticated feature engineering to augment the data representation and improve model outcomes, as evidenced by Logistic Regression and SVM's perfect F1 of 0.80 showing their clinical utility. It ends also with challenges, such as the need for large, annotated datasets and how to deal with the interpretability of complex models, as well as a set of actionable recommendations for improving diagnostics methodologies. The findings of this work highlight the role of machine learning in cervical cancer diagnosis and its potential to improve patient outcomes through early detection and prevention.