Data Driven Prognosis of Cervical Cancer Using Class Balancing and Machine Learning Techniques

Abstract

INTRODUCTION: With the progression of innovation and its joint effort with health care services, the world has achieved a lot of benefits. AI procedures and machine learning techniques are constantly improving existing statistical methods for better results in the medical field. These improved methods will assist health care providers in providing intelligent medical services.

OBJECTIVES: This Cervical cancer is the fourth most common cancer among the other female cancers. This cancer is preventable with early diagnosis. This reason becomes the motivation of the research work. For efficiently and timely prognosis of cervical cancer require a computer-assisted algorithm.

METHODS: The work demonstrated in this paper contributes to the techniques of machine learning for diagnosing cervical cancer. The machine learning algorithms used in this research are K Nearest Neighbour, Support Vector Machine and Random Forest Tree. These classification algorithms are used with class balancing techniques including undersampling, Oversampling and SMOTE.

RESULTS: The evaluation metrics used for comparative analysis includes accuracy, sensitivity, specificity, negative predicted accuracy, and positive predictive accuracy. The results show the Random Forest algorithm with SMOTE technique delivered more promising results over SVM and KNN for four target variables Schiller, Biopsy, Hinselmann , and Cytology respectively.

CONCLUSION: It is concluded that with the limited amount of data which also suffers from the unbalancing problem the promising results drawn using the proposed model.