1. Improving cirrhosis stage prediction accuracy with the new CatBoost classifier algorithm and comparing it with the Adaboost classifier.
- Author
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Charan, K. E., Bhavani, N. P. G., and Thiruchelvam, V.
- Subjects
- *
FEATURE extraction , *CIRRHOSIS of the liver , *ALGORITHMS , *FORECASTING - Abstract
The main objective of this work is to perform Cirrhosis Stage using Novel CatBoost Classifier Algorithm and Compare it with the Adaboost Classifier Algorithm to improve the accuracy. It went over the in-depth evaluations of recommended algorithms. Ten testing samples were conducted for each algorithm using the Cirrhosis Dataset, which was downloaded from the Kaggle.com website. For the SPSS study, close to ten samples were gathered in order to evaluate, compare, and determine the effectiveness of recommended algorithms. An alpha of 0.05 and a G power of 0.85 are used by the SPSS software. Ten samples each from the Novel CatBoost algorithm group 1 and the Adaboost algorithm group 2 are gathered in order to assess the predictive accuracy of Cirrhosis. We presented and spoke about the results of combining several feature extraction methods with classifiers. Novel CatBoost was shown to have the highest level of accuracy, scoring 92.1%. Based on the independent sample t-test, there is a statistically significant difference between the two groups (p=0.001; p<0.05). to evaluate the precision of the Cirrhosis stage prediction. To improve the accuracy of the Cirrhosis Stage prediction, increasing forecast precision is a very promising approach. The results of this study show that the Novel CatBoost algorithm performs significantly better than the Adaboost approach. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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