LSTM prediction probability with sigmoid is low and so is AUC-CodePudding

I have a timeseries data of 3228 Patients and I am doing a disease (Sepsis) forecasting using LSTM

How I can improve the model to get higher probabilities and high AUC? I tried without BatchNormalization() but no increase in probabilities. I tried without mask(), I tried increasing LSTM Layers, I tried by changing optimizer to Adam, even changing learning rate but no better results.

CodePudding user response：

Had a look at your data and the reason you keep hitting 0.86 accuracy is that the incidence of the positives in your data is roughly ~14%. Accuracy is probably not the best metric to track here given the imbalance (perhaps area under precision-recall curve would be better). You might also try using the sample_weight argument of the fit function to weight your samples and counteract the imbalance issue.

CodePudding user response：

What I would try, but no promises :) Feed the hidden layers of the LSTM into a dense layer and then to the binary output.