Data Driven Prognosis of Cervical Cancer Using ClassBalancing and Machine Learning Techniques

被引：3
作者：
Arora M. [1 ,2 ]
Dhawan S. [1 ,3 ]
Singh K. [1 ,3 ]
机构：
[1] Department of Computer Science & Engineering, University Institute of Engineering & Technology (U.I.E.T), Kurukshetra University, Kurukshetra
[2] Department of Computer Science and Technology, Manav Rachna University, Faridabad
[3] Faculty of Computer Science & Engineering, Department of Computer Science & Engineering, University Institute of Engineering & Technology (U.I.E.T), Kurukshetra University, Kurukshetra
来源：
EAI Endorsed Transactions on Energy Web | 2020年 / 7卷 / 30期
关键词：
Cervical Cancer; K-Nearest Neighbour; Random forest; Random over-sampling; random under-sampling; SMOTE; Support vector machine;
D O I：
10.4108/eai.13-7-2018.164264
中图分类号：
学科分类号：
摘要：
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 under-sampling, 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. ©2020 Mamta Arora et al., licensed to EAI. This is an open access article distributed under the terms of theCreative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
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页码：1 / 9
页数：8
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