My task is to predict how many years a person has left to live using an MLP. There is one specific feature I'd like to discuss: current age.
Statistically, it's a conditional probability. Example: When 0 years old, I'm expected to live until 70 years old (70 years left). However when I'm 70 years old, I'm expected to live until 83 (13 years left).
In my dataset, I have the true age of when someone died, and it follows a distribution. I have thus augmented my data, if someone died at the age of N, then there will be N datapoints of current age(feature) from 0 -> N and years left to live (target) from N -> 0 accordingly.
Through this augumentation, I'm hoping to model the distribution of current age vs years left to live through the data. I would never want my network to output (after denormalization) that if the current age was 70, the person would have 50 years left.
Now, the best practice of an MLP states that 1-2 hidden layers is enough, and amount of nodes in the hidden layers should be between the amount of input and output layers. However if I just normalized age through feature scaling, I would have one input node, one hidden node and one output node. This is realistically not enough, I'd expect that I need a lot of hidden nodes.
If I used one hot encoding that represents current age from 0 to 100. I'd be able to use 50 hidden nodes through good practice. If I was 70 years today, I would probably never activate the node that will tell me I'd have another 50 years to live.
My question then, is that should I skip the one hot encoding, and just scale the age and use for example 50 hidden nodes?
Theoretically, because age is continuous as not as distinct nor discrete as say "cat" and "robot" then one-hot is not necessary, but is there any drawbacks if I choose to one-hot encode?