I am trying to make a model to classify whether these patients can be diagnosed with dementia by their 35 days of biometric data.
A brief summary of a dataset is below.
as an input y_train data, it has a list of 148 patient's diagnoses. It can be 'CN' 'MCI' and 'Dem', so it has 3 categories.
['CN', 'MCI', 'MCI', 'CN', 'MCI', 'CN', 'MCI', ...
So I made a model something like this.
model = Sequential() model.add(LSTM(35,input_shape=(35, 51))) #35 days, 51 features model.add(Dropout(0.5)) model.add(Dense(100, activation='relu')) model.add(Dense(3, activation='softmax')) model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
Which accuracy is really not that great, as you can see below it does not even pass validation accuracy 50%.
... Epoch 200/200 2/2 [==============================] - 0s 131ms/step - loss: 0.0609 - sparse_categorical_accuracy: 0.9835 - val_loss: 3.2440 - val_sparse_categorical_accuracy: 0.4667
Is there any way to improve this model? Or should I have to rebuild the model?