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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 115
Article Submitted : 19698
Article Published : 8087
Total Authors : 21392
Total Reviewer : 770
Total Countries : 147
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| Paper Title: | Cervical Cancer Detection Using Machine Learning |
| Authors Name: | M. Sai Suma , S. Deepika , A. Sowmya Reddy , Ch. Manideep Reddy |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_186107
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| Published Paper Id: | IJRTI2304148 |
| Published In: | Volume 8 Issue 4, April-2023 |
| DOI: | |
| Abstract: | Cervical cancer is one of the most deadly diseases in the world among women. It is caused by long term infection in skin cells and mucous membrane cells of the genital area. The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The goal of this research paper is to utilize both exploratory data analysis (EDA) and machine learning algorithms to gain a thorough understanding of the risk factors associated with cervical cancer, as well as to develop a dependable approach for detecting the disease. Exploratory data analysis (EDA) is an essential step in understanding the patterns and relationships that exist within a dataset. In the case of cervical cancer, EDA can help us identify the factors that are most likely to be responsible for the development of the disease. By exploring the data, we can gain insight into the various risk factors associated with cervical cancer, including age, sexual activity, family history, and exposure to the human papillomavirus (HPV). The KNeighbors Classifier with n_neighbors = 2 using normalization achieved the highest accuracy of 0.9992, indicating that this model was able to predict cervical cancer with a high degree of accuracy. |
| Keywords: | Cervical Cancer, Exploratory Data Analysis, Machine Learning, Human Papillomavirus, Normalization. |
| Cite Article: | "Cervical Cancer Detection Using Machine Learning", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 4, page no.902 - 909, April-2023, Available :http://www.ijrti.org/papers/IJRTI2304148.pdf |
| Downloads: | 000205227 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2304148
Registration ID:186107
Published In: Volume 8 Issue 4, April-2023
DOI (Digital Object Identifier):
Page No: 902 - 909 Country: Suryapet, Telangana, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2304148 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2304148 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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