IJBTT

Liver Infection Prediction Analysis using Machine Learning to Evaluate Analytical Performance in Neural Networks by Optimization Techniques

Liver Infection Prediction Analysis using Machine Learning to Evaluate Analytical Performance in Neural Networks by Optimization Techniques
	© 2023 by IJETT Journal
	Volume-71 Issue-3
	Year of Publication : 2023
	Author : P. Deivendran, S. Selvakanmani, S. Jegadeesan, V. Vinoth Kumar
	DOI : 10.14445/22315381/IJETT-V71I3P240

How to Cite?

P. Deivendran, S. Selvakanmani, S. Jegadeesan, V. Vinoth Kumar, "Liver Infection Prediction Analysis using Machine Learning to Evaluate Analytical Performance in Neural Networks by Optimization Techniques," International Journal of Engineering Trends and Technology, vol. 71, no. 3, pp. 377-384, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I3P240

Abstract
Liver infection is a common disease, which poses a great threat to human health, but there is still able to identify an optimal technique that can be used on large-level screening. This paper deals with ML algorithms using different data sets and predictive analyses. Therefore, machine ML can be utilized in different diseases for integrating a piece of pattern for visualization. This paper deals with various machine learning algorithms on different liver illness datasets to evaluate the analytical performance using different types of parameters and optimization techniques. The selected classification algorithms analyze the difference in results and find out the most excellent categorization models for liver disease. Machine learning optimization is the procedure of modifying hyperparameters in arrange to employ one of the optimization approaches to minimise the cost function. To set the hyperparameter, include a number of Phosphotase,Direct Billirubin, Protiens, Albumin and Albumin Globulin. Since it describes the difference linking the predictable parameter's true importance and the model's prediction, it is crucial to minimise the cost function.

Keywords
Classification, Neural networks, Linear regression, Random forest, Naïve-Bayes.

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