I have clinical data which lacks a golden standard for diagnosis. instead, multiple indices are used with different accuracy. I have transformed the results from these indices to binary for patients having and not having the disease. This is then used for latent classification analysis. I want to know if I should use all samples for this analysis. or should I divide them and then use the remaining samples for validation? Also, will I need to check for verification bias and how do I select a method to do that? How to check the category of each patient for diagnosis after that?

latent classification analysis (LCA) is performed by using the poLCA package in R

results that I get are as follow

Fit for 2 latent classes:

number of observations: 65 number of estimated parameters: 7 residual degrees of freedom: 0 maximum log-likelihood: -116.6411

AIC(2): 247.2821 BIC(2): 262.5028 G^2(2): 0.3041395 (Likelihood ratio/deviance statistic) X^2(2): 0.291966 (Chi-square goodness of fit)

what type of graph will make me understand the results better? Thank you, your help will be appreciated here