Simple neural network implementation in keras
Considering the nature of your problem (disease classification?), probably you have imbalanced data: more negative/benign w.r.t. positive/malicious. In such cases accuracy is not a good metric to assess the quality of a model.

Consider, as example, a dataset with 99 "negative" cases and only one "positive" case, and consider a classifier which predict "negative" all the times. The accuracy of such model is, actually, 99%:



What you want to do in order to deal with unbalanced data is:

1) just use another metric, as ROC-AUC. This can be sufficient if you have a dataset not so much unbalanced, say up to 1:20. This metric is not officially supported in Keras, but you can find a custom callback which calculate the AUC score every end of epoch here.

2) if the imbalance is very big, say 1:1000, you need to balance the dataset. There are lots of techniques, the simplest one is to make copies of the minority class samples, as many as needed to reach the balance. Other smarter techniques exist, and some of then are included in imbalanced-learn for SKLearn.

If you want to have a better overview of techniques and metrics used in such cases, I suggest you this paper.