TELKOMNIKA Telecommunication Computing Electronics and Control
An approach of cervical cancer diagnosis using class weighting and oversampling with Keras
Dublin Core
Title
TELKOMNIKA Telecommunication Computing Electronics and Control
An approach of cervical cancer diagnosis using class weighting and oversampling with Keras
An approach of cervical cancer diagnosis using class weighting and oversampling with Keras
Subject
Cervical cancer diagnosis
Class weighting
Logit model
Machine learning
Oversampling
Prediction of cervical cancer
Class weighting
Logit model
Machine learning
Oversampling
Prediction of cervical cancer
Description
Globally, cervical cancer caused 604,127 new cases and 341,831 deaths in
2020, according to the global cancer observatory. In addition, the number of
cervical cancer patients who have no symptoms has grown recently.
Therefore, giving patients early notice of the possibility of cervical cancer is
a useful task since it would enable them to have a clear understanding of
their health state. The use of artificial intelligence (AI), particularly in
machine learning, in this work is continually uncovering cervical cancer.
With the help of a logit model and a new deep learning technique, we hope
to identify cervical cancer using patient-provided data. For better outcomes,
we employ Keras deep learning and its technique, which includes class
weighting and oversampling. In comparison to the actual diagnostic result,
the experimental result with model accuracy is 94.18%, and it also
demonstrates a successful logit model cervical cancer prediction.
2020, according to the global cancer observatory. In addition, the number of
cervical cancer patients who have no symptoms has grown recently.
Therefore, giving patients early notice of the possibility of cervical cancer is
a useful task since it would enable them to have a clear understanding of
their health state. The use of artificial intelligence (AI), particularly in
machine learning, in this work is continually uncovering cervical cancer.
With the help of a logit model and a new deep learning technique, we hope
to identify cervical cancer using patient-provided data. For better outcomes,
we employ Keras deep learning and its technique, which includes class
weighting and oversampling. In comparison to the actual diagnostic result,
the experimental result with model accuracy is 94.18%, and it also
demonstrates a successful logit model cervical cancer prediction.
Creator
Hieu Le Ngoc, Khanh Vo Pham Huyen
Source
http://telkomnika.uad.ac.id
Date
Nov 16, 2022
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
Hieu Le Ngoc, Khanh Vo Pham Huyen, “TELKOMNIKA Telecommunication Computing Electronics and Control
An approach of cervical cancer diagnosis using class weighting and oversampling with Keras,” Repository Horizon University Indonesia, accessed April 28, 2025, https://repository.horizon.ac.id/items/show/4446.
An approach of cervical cancer diagnosis using class weighting and oversampling with Keras,” Repository Horizon University Indonesia, accessed April 28, 2025, https://repository.horizon.ac.id/items/show/4446.