Early Detection of Cervical Cancer using Machine Learning Classifiers for Improved Diagnosis in Underserved Regions
- Title
- Early Detection of Cervical Cancer using Machine Learning Classifiers for Improved Diagnosis in Underserved Regions
- Creator
- Santhiya S.; Mapari S.; Abinaya N.; Jayadharshini P.; Priyanka S.; Krishnasamy L.
- Description
- One of the incurable diseases that affect women is cervical cancer. It is brought on by a protracted infection of the skin and the vaginal mucous membrane cells. The Human Papilloma Virus (HPV), is the main factor causing aberrant cell proliferation in the area around the cervix. There are no symptoms present when the illness first appears. Early detection of this malignancy may be used to prevent death. People in less developed countries cannot afford to periodically examine themselves due to a lack of awareness, poor medical infrastructure, and expensive medication. The EDA technique is applied to examine the data and understand its characteristics. Machine Learning algorithm has been used to diagnose cervical cancer. In order to spot the existence of cervical cancer, five machine learning classifiers are utilized, the algorithms to begin earlier. The Logistic Regression classifier's results validate the correct stage prediction. 2023 IEEE.
- Source
- 2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023, pp. 582-587.
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Benign; Cervical Cancer; Logistic Regression; Malignant; ML; Random Forest
- Coverage
- Santhiya S., Kongu Engineering College, Department of Artificial Intelligence, TamilNadu, Erode, India; Mapari S., Symbiosis International (Deemed University), Symbiosis Institute of Computer Studies and Research (SICSR), Pune, India; Abinaya N., Kongu Engineering College, Department of Artificial Intelligence, TamilNadu, Erode, India; Jayadharshini P., Kongu Engineering College, Department of Artificial Intelligence, TamilNadu, Erode, India; Priyanka S., Kongu Engineering College, Department of Artificial Intelligence, TamilNadu, Erode, India; Krishnasamy L., Christ (Deemed to Be) University, School of Engineering and Technology, Department of Computer Science and Engineering, Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835034438-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Santhiya S.; Mapari S.; Abinaya N.; Jayadharshini P.; Priyanka S.; Krishnasamy L., “Early Detection of Cervical Cancer using Machine Learning Classifiers for Improved Diagnosis in Underserved Regions,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19648.