OPTIMIZED ENSEMBLE MACHINE LEARNING BASED FRAMEWORK FOR EARLY DETECTION OF LIVER DISEASE
Keywords:
Ensemble machine learning, Liver disease diagnosis, Performance metrics, Ensemble voting classifier, Diagnostic accuracyAbstract
The proposed framework integrates multiple machines learning algorithms, including decision trees,support vector machines, and neural networks, to form an ensemble model that leverages the strengths of eachindividual technique. By employing advanced optimization techniques such as grid search and geneticalgorithms, the framework fine-tunes hyperparameters to achieve optimal performance
References
M. Ghosh et al., “A comparative analysis of machine learning algorithms to predict liver disease,” Intell. Autom. Soft Comput., Vol. 30, No. 3, pp. 917–928, 2021.
Jagdeep Singha et al. “Software Based Prediction of Liver Disease with Feature Selection and Classification Techniques” ELSEVIER 2020.
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Published
2024-11-20
How to Cite
Anita Meghta,Dr. Kapil Sethi. (2024). OPTIMIZED ENSEMBLE MACHINE LEARNING BASED FRAMEWORK FOR EARLY DETECTION OF LIVER DISEASE. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 5435–5448. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3217
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