One-way ANOVA for multiple features? I have 60 continuous features (i.e., lab results) for 35,000 patients, and the patients are divided into three groups. I want to evaluate each feature to find out what the differences are between the groups.
Here is my plan:

*

*For each feature, iteratively run a one-way ANOVA independently.

*After 60 ANOVA are run, correct for multiple comparisons using Bonferroni correction.

*Using only the significant features (as per the ANOVA with Bonferroni), run a post-hoc analysis (Tukey's HSD) for each one.

*Use the p-value results from Tukey's HSD to confirm any significant pairwise differences between the groups. Assume there is no need for additional Bonferroni correction at this stage.

Is this an acceptable approach? Or do I need to use a multi-way ANOVA for this? Or MANOVA?

Try (multinomial) logistic regression. That way you will avoid multiplicity problems, and also the overly strict assumptions behind discriminant analysis.
This is very close to the post T-tests, manova or logistic regression - how to compare two groups?, which is similar but for two groups.
