How to transform specific type feature to yield better prediction? I have a dataset with 5K records focused on binary classification problem. I have about 60 features.

Out of 60 features, around 45-46 features are of 'Min' and 'Max' type.

For example, minimum blood pressure, maximum old pressure, minimum heart rate, maximum heart rate, minimum potassium, maximum potassium etc. 

Several other vital parameters like sodium, urea etc. follow this pattern of min and max. 

Any suggestions on how can I transform them without losing info . currently when I use as-is,  I get only around 86% accuracy with recall value of minority class only around 70. Resamplin didn't help much. 

Your suggestions and experience in how to transform this to yield better predictions would really be helpful.
ML has sometimes problems to find relations obvious for people, so I would help a little bit by creating a new feature:

relX = (maxX-minX)/minX


because some diseases are more correlated with jumps of some parameters than with their level, however, such relX may be too much correlated with target and may cause overfitting.
Usually, after creating such a feature, it is better to remove one of the original features, sometimes both.