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. 2024 Aug 27;14(1):19828.
doi: 10.1038/s41598-024-67562-0.

Integrating cat boost algorithm with triangulating feature importance to predict survival outcome in recurrent cervical cancer

S Geeitha  1 , K Ravishankar  2 , Jaehyuk Cho  3 , Sathishkumar Veerappampalayam Easwaramoorthy  4
Affiliations

Integrating cat boost algorithm with triangulating feature importance to predict survival outcome in recurrent cervical cancer

S Geeitha et al. Sci Rep. .

Abstract

Cervical cancer is one of the most dangerous malignancies in women. Prolonged survival times are made possible by breakthroughs in early recognition and efficient treatment of a disease.The existing methods are lagging on finding the important attributes to predict the survival outcome. The main objective of this study is to find individuals with cervical cancer who are at greater risk of death from recurrence by predicting the survival.A novel approach in a proposed technique is Triangulating feature importance to find the important risk factors through which the treatment may vary to improve the survival outcome.Five algorithms Support vector machine, Naive Bayes, supervised logistic regression, decision tree algorithm, Gradient boosting, and random forest are used to build the concept. Conventional attribute selection methods like information gain (IG), FCBF, and ReliefFare employed. The recommended classifier is evaluated for Precision, Recall, F1, Mathews Correlation Coefficient (MCC), Classification Accuracy (CA), and Area under curve (AUC) using various methods. Gradient boosting algorithm (CAT BOOST) attains the highest accuracy value of 0.99 to predict survival outcome of recurrence cervical cancer patients. The proposed outcome of the research is to identify the important risk factors through which the survival outcome of the patients improved.

Keywords: Attribute selection; Cervival cancer; Gradient boosting; Recurrence; ReliefF.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Working procedure of the proposed system.
Figure 2
Figure 2
Working procedure of catboost Gradient boosting classifier.
Figure 3
Figure 3
Catboost classifier structure.
Figure 4
Figure 4
Statistical representation of numerical data.
Figure 5
Figure 5
Feature score of ReliefF, Information gain, FCBF.
Figure 6
Figure 6
Feature scores of 8 Features.
Figure 7
Figure 7
Important feature selection.
Figure 8
Figure 8
Four different model parameters.
Figure 9
Figure 9
Mean TP and FP at the threshold for GB.
Figure 10
Figure 10
ROC Curves of six classifiers.
Figure 11
Figure 11
Performance of lift curve.
Figure 12
Figure 12
Performance curve of cumulative gain.
Figure 13
Figure 13
Performance curve of precision and recall.
Figure 14
Figure 14
Calibration curve of a Cat boost model(Sigmoid calibration).
Figure 15
Figure 15
Classification accuracy of Catboost model.
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