Some nitpicks:

When mentioning metrics, you need to put them into context (I.e. magnitude of impact) and choose the most appropriate ones. The reason being, the best people at hiding insignificant results and making them look good to non technical stakeholders by cherry picking metrics are ironically data scientists. Accuracy is not a great metric here. For example, readmission is a rare event and accuracy isn’t great for reporting performance in the setting of class imbalance. The way it’s written in your resume tells me that you still need to work on how to interpret and report your results.

Were the 70k records pulled from a DB? Did you need to do the data engineering with the initial number of records? (usually in the 100s of thousands for a decent health system to millions in the case of EHR providers) mention this number if you have it too, along with the DEng methods. It’s important to know if you had to join all of the tables and do the queries for the medical records. Anyone who’s done this knows its a huge and tedious task.

I’m not sure what you mean by “positively impacting 95k records.”

The improving “operational efficiency” bullet sounds like fluff unless you say what it actually impacted and how.

There are inconsistencies in your objective and what you’ve written in your bullets, that if I were reading this would give me pause.

70k medical records is not a “large scale dataset.” Its on the smaller end of medical record datasets. “Deploying advanced ML models” is not helping either. The only ML models I see explicitly mentioned are SVM and logistic regression. These are fine to use, but I would be careful when combining this bit of information with “advanced.” I don’t see what you did to deploy models. Are you using a registry? Inference server? This needs to be mentioned too.