Bidirectional recurrent neural network approach for predicting cervical cancer recurrence and survival

Abstract Cervical cancer is a deadly disease in women globally. There is a greater chance of getting rid of cervical cancer in case of earliest diagnosis. But for some patients, there is a chance of recurrence. The chances of treating the Recurrence of cervical carcinoma arelimited. The main objecti...

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Main Authors: S. Geeitha, K. P. Rama Prabha, Jaehyuk Cho, Sathishkumar Veerappampalayam Easwaramoorthy
Format: Article
Language:English
Published: Nature Portfolio 2024-12-01
Series:Scientific Reports
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Online Access:https://doi.org/10.1038/s41598-024-80472-5
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author S. Geeitha
K. P. Rama Prabha
Jaehyuk Cho
Sathishkumar Veerappampalayam Easwaramoorthy
author_facet S. Geeitha
K. P. Rama Prabha
Jaehyuk Cho
Sathishkumar Veerappampalayam Easwaramoorthy
author_sort S. Geeitha
collection DOAJ
description Abstract Cervical cancer is a deadly disease in women globally. There is a greater chance of getting rid of cervical cancer in case of earliest diagnosis. But for some patients, there is a chance of recurrence. The chances of treating the Recurrence of cervical carcinoma arelimited. The main objective of a research is to find the key features that will predict the cervical cancer recurrence and survival rates accurately by utilizing a neural network that is bidirectionally recurrent. The goal is to reduce risk factors of cervical cancer recurrence by identifying genes with positive coefficients and targeting them for preventive interventions. First step is identification of risk factors for cervical carcinoma recurrence by utilising clinical attributes. This research uses following Random forest, Logistic regression, Gradient boosting and support vector machine algorithms are applied for classification. Random forest offers the maximum precision of these four techniques at 91.2%. The second step is identifying long noncoding RNA (lnRNA) gene signatures among people with cervical carcinomaby implementingHSIC model. Intended to discover biomarkers in initial cervical carcinoma clinical data from people who experienced a distant repetition that could be connected to lnRNA gene signatures and utilized for forecasting survival rates using a bidirectional recurrent neural network(Bi-RNN). The results shows that Bi-RNN model effectively forecast the cervical cancer recurrence and survival.
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institution Kabale University
issn 2045-2322
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spelling doaj-art-40fa705d037d4eaca876be592b67cb632025-01-05T12:24:41ZengNature PortfolioScientific Reports2045-23222024-12-0114111510.1038/s41598-024-80472-5Bidirectional recurrent neural network approach for predicting cervical cancer recurrence and survivalS. Geeitha0K. P. Rama Prabha1Jaehyuk Cho2Sathishkumar Veerappampalayam Easwaramoorthy3Department of Information Technology, M. Kumarasamy College of EngineeringSchool of Computer Science and Engineering, Vellore Institute of TechnologyDepartment of Software Engineering & Division of Electronics and Information Engineering, Jeonbuk National UniversitySchool of Engineering and Technology, Sunway UniversityAbstract Cervical cancer is a deadly disease in women globally. There is a greater chance of getting rid of cervical cancer in case of earliest diagnosis. But for some patients, there is a chance of recurrence. The chances of treating the Recurrence of cervical carcinoma arelimited. The main objective of a research is to find the key features that will predict the cervical cancer recurrence and survival rates accurately by utilizing a neural network that is bidirectionally recurrent. The goal is to reduce risk factors of cervical cancer recurrence by identifying genes with positive coefficients and targeting them for preventive interventions. First step is identification of risk factors for cervical carcinoma recurrence by utilising clinical attributes. This research uses following Random forest, Logistic regression, Gradient boosting and support vector machine algorithms are applied for classification. Random forest offers the maximum precision of these four techniques at 91.2%. The second step is identifying long noncoding RNA (lnRNA) gene signatures among people with cervical carcinomaby implementingHSIC model. Intended to discover biomarkers in initial cervical carcinoma clinical data from people who experienced a distant repetition that could be connected to lnRNA gene signatures and utilized for forecasting survival rates using a bidirectional recurrent neural network(Bi-RNN). The results shows that Bi-RNN model effectively forecast the cervical cancer recurrence and survival.https://doi.org/10.1038/s41598-024-80472-5Recurrence cervical CancerRecurrent neural networklnRNAMachine learningRisk factors
spellingShingle S. Geeitha
K. P. Rama Prabha
Jaehyuk Cho
Sathishkumar Veerappampalayam Easwaramoorthy
Bidirectional recurrent neural network approach for predicting cervical cancer recurrence and survival
Scientific Reports
Recurrence cervical Cancer
Recurrent neural network
lnRNA
Machine learning
Risk factors
title Bidirectional recurrent neural network approach for predicting cervical cancer recurrence and survival
title_full Bidirectional recurrent neural network approach for predicting cervical cancer recurrence and survival
title_fullStr Bidirectional recurrent neural network approach for predicting cervical cancer recurrence and survival
title_full_unstemmed Bidirectional recurrent neural network approach for predicting cervical cancer recurrence and survival
title_short Bidirectional recurrent neural network approach for predicting cervical cancer recurrence and survival
title_sort bidirectional recurrent neural network approach for predicting cervical cancer recurrence and survival
topic Recurrence cervical Cancer
Recurrent neural network
lnRNA
Machine learning
Risk factors
url https://doi.org/10.1038/s41598-024-80472-5
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AT kpramaprabha bidirectionalrecurrentneuralnetworkapproachforpredictingcervicalcancerrecurrenceandsurvival
AT jaehyukcho bidirectionalrecurrentneuralnetworkapproachforpredictingcervicalcancerrecurrenceandsurvival
AT sathishkumarveerappampalayameaswaramoorthy bidirectionalrecurrentneuralnetworkapproachforpredictingcervicalcancerrecurrenceandsurvival