# 3 ways you can beat machine learning

Machine learning is coming to medicine. Even though I am a fan of computing even I have been shocked at the speed of the advancement of machine learning in medicine. We can discuss the ethics, we can proclaim that it’s not fair, however, this will not change the fact that machine learning is coming. A prominent example was the prediction of heart attacks. Not only was the machine learning algorithm for accurate that consultants it even considered parameters like if the patient was taking steroids, and the patient’s mental health. Bizarrely, it was more comprehensive than a human [link]. Does this mean that you’re going to be replaced? Of course not. Here we can discuss the areas to focus on to minimise the effect of machine learning.

To be effective, we must discuss what machine learning is. In brief, it’s an equation with several variables. Let’s say that we want to predict a patient’s blood pressure by their age. We all know this would be a terrible algorithm, it’s more for demonstration. We would have the following prediction model:

BP = (weighting_A x age) + (weighting_B x pulse)

A load of training data is pushed through this equation and the results are compared with real results and the error is calculated. There’s a bunch of steps and this is where the science of machine learning gets complicated but the result is that the weighting for each variable is slightly altered to achieve less of an error. This cycle is repeated. Step size, conditioning before calculating the rate of change to avoid local minimas, logistic functions to account for yes/no answers and neural networks are just some of the things to consider that cannot be covered in a single post. However, the take home message here is that the machine learning algorithm tweaks the weighting of each variable until it comes up with an optimum prediction model. The advantage of computing is that you can do this with thousands of variables. It can consider a variable even if it only contributes 0.001% to the outcome. Trying to compete with this is not an effective use of your time. There is so much more to you than pattern recognition and memorisation. Here are some areas that you have time focus on now Google and machine learning are focusing on pattern recognition and memorisation.

Get Personal