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Prediction of Cervical Cancer using Machine Learning and Deep Learning Algorithms

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Prediction of Cervical Cancer using Machine Learning and Deep Learning Algorithms


Kayalvizhi. K. R | N Kanimozhi



Kayalvizhi. K. R | N Kanimozhi "Prediction of Cervical Cancer using Machine Learning and Deep Learning Algorithms" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6, October 2020, pp.426-430, URL: https://www.ijtsrd.com/papers/ijtsrd33378.pdf

As the development of machine learning and deep learning, more and more people or organizations use multiple algorithms to analyse large collections of data to produce meaningful results that help to predict behaviour. And this kind of technology is increasingly used in medical field to predict some severe illness in their early stage, for example, cervical cancer. Cervical Cancer is one of the main reasons of deaths in countries having a low capita income. It is the second most common cancer in India in women accounting for 22.86% of all cancer cases in women. It becomes quite complicated while examining a patient on the basis of result obtained from various doctor’s preferred test to determine if the patient is positive with the cancer. There were 96,922 new cases of cervical cancer diagnosed in India in 2018. Around the globe, around a quarter of million people die owing to cervical cancer. Screening and different deterministic tests confuse the available Computed Aided Diagnosis (CAD) to treat the patient correctly for the cancer. Machine learning and Deep learning algorithms are used in this project and determine if the patient has cancer based on the analyses of the risk factors available in the dataset. Predicting the presence of cervical cancer can help the diagnosis process to start at an early stage and comparing various models will help in finding out the best prediction model for predicting the presence of cervical cancer effectively.

Cervical Cancer, Machine learning, Deep learning, Logistic regression, SVM, Decision Tree, Random Forest, Deep Neural networks, Dataset


IJTSRD33378
Volume-4 | Issue-6, October 2020
426-430
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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