Liver Infection Prediction Analysis using Machine Learning to Evaluate
  Analytical Performance in Neural Networks by Optimization Techniques
P. Deivendran S. Selvakanmani S. Jegadeesan V. Vinoth Kumar
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.