I want to predict the outcome of a particular treatment (remitted or not) using demographic, plasma biomarker, genetic, and clinical data. IS a neural network model the best way of doing this? What advantages does this have over traditional logistic regression model building? How limited am I with only 120 cases and up to 40 covariates, depending on collinearity? How do I pare these down? I would normally tend towards factor analysis but will a neural net combine collinear variables sensibly? Any ideas on combining multimodal data like that would be helpful, or a starting point for reading - already have Ripley's MASS.
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It's often a good idea to do PCA before fitting a neural network, so your instinct could be right there. The only way you are going to determine which model is better for a given problem is to cross-validate both and compare out-of-sample error. The caret package in R is a good way to compare models using this technique (specifically the train function). As a bonus, it includes a model call pcaNNet which calculates principle components before fitting a neural network. |
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General rules for when to use a neural network: 1) you can tell, relatively easily, what the right answer is, but not describe how you know that's the right answer; if you know what steps to take to get the right answer, then code it rather than training a NN, and if you can't tell what the right answer is likely to be, likely a NN won't be able to either 2) 90% accuracy is good enough (e.g. when other techniques give substantially less); NN by their nature do not give watertight 100% accuracy 3) you just need the right answer, not an understanding of how; NN's do not, by their nature, tend to give much insight into the nature of the system By the way, giving a NN both the raw data and transforms of it (averages, deltas, etc.) and letting the learning algorithm decide which are useful for prediction is better than figuring it out yourself; if you determine everything about which factors are important and how to code them, you have done most of the work (not all) which a NN can do for you anyway. p.s. running a NN many times and taking the best result is a good idea; any good NN implementation is stochastic, and different runs may be better or worse by a substantial amount. |
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