A Comparative Analysis of Classification Techniques for Cervical Cancer Utilising At Risk Factors and Screening Test Results
Published in The 27th Irish Conference on Artificial Intelligence and Cognitive Science, 2019
Abstract
Cervical cancer is a severe concern for women’s health. Every year in the Republic of Ireland, approximately 300 women are diagnosed with cervical cancer, 30% for whom the diagnosis will prove fatal. It is the second most common cause of death due to cancer in women aged 25 to 39 years. Recently there has been a series of controversies concerning the mishandling of results from cervical screening tests, delays in processing said tests and the recalling of individuals to retake tests. The serious nature of the prognosis highlights the importance and need for the timely processing and analysis of data related to screenings. This work presents a comparative analysis of several classification techniques used for the automated analysis of known risk factors and screening tests with the aim of predicting cervical cancer outcomes via a Biopsy result. These techniques encompass methods such as tree-based, cluster-based, liner and ensemble techniques, and where applicable use parameter tuning to determine optimal model parameters. The dataset utilised for training and validation consists of 858 observations and 36 variables, including the binary target variable “Biopsy”. The data itself is heavily imbalanced with 803 negative and 55 positive observations with approximately 11.73% of the data points missing. These issues are addressed during pre-processing by methods such as mean or median imputation, as well as over-sampling, under-sampling and combination techniques which led to the creation of 6 augmented datasets of varying size, consisting of 34 variables including the response Biopsy. The results show that a SMOTE-Tomek combination resampling method in conjunction with a tuned Random Forest model produced an accuracy score of 99.69% with a recall and precision value of 0.99% for both positive and negative responses.
Recommended citation: Sean Quinlan, Haithem Afli and Ruairi O'Reilly (2019). “A Comparative Analysis of Classification Techniques for Cervical Cancer Utilising At Risk Factors and Screening Test Results.” The 27th Irish Conference on Artificial Intelligence and Cognitive Science.
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