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. 2024 Dec 30;14(1):31641.
doi: 10.1038/s41598-024-80472-5.

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

S Geeitha  1 , K P Rama Prabha  2 , Jaehyuk Cho  3 , Sathishkumar Veerappampalayam Easwaramoorthy  4
Affiliations

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

S Geeitha et al. Sci Rep. .

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.

Keywords: Machine learning; Recurrence cervical Cancer; Recurrent neural network; Risk factors; lnRNA.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Proposed technique.
Fig. 2
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ROC curve of random forest algorithm.
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Fig. 3
Feature ranking based on HSIC and MKL.
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Fig. 4
Box plot of different recurrence survivor and healthy control patients.
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Fig. 5
Bidirectional recurrent neural network architecture.
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Volcano plot.
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Mean difference plot.
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Expression density plot.
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UMAP plot.
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Fig. 10
P Value histogram.
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