Comparison of Multi-Label Classification Methods for Prediagnosis of Cervical Cancer

Abstract

Cervical cancer is one of the most common causes of cancer death of women. Prediagnosis of cervical cancer at early stages is critical to reduce mortality ratios. Additionally, early prediction of cervical cancer can help both the patients and the physicians depending on easiness of treatment. Cervical cancer results from various risk factors such as family history, education level, having multiple full-term pregnancies, smoking, and sexually transmitted diseases and etc. Recently, different types of advanced methods were developed for risk prediction analysis based on machine learning techniques. The purpose of this study is to investigate the efficacy of using multi-label classification techniques for diagnosing cervical cancer at early stage. Four common learning algorithms such as Naive Bayes, J48 Decision Tree, Sequential Minimal Optimization, and Random Forest were compared in terms of their accuracy, hamming loss, exact match (subset accuracy) and ranking loss performance evaluation metrics. Thus, this study can help to physicians, academics and cancer researchers to make fast and accurate diagnosis.