I am new to neural networks. I am trying to solve a binary classification problem. Specifically, I want to determine whether a patient has or not a certain disease based on the dataset.
The dataset has about 700 samples of different patients. I divided the sets into training and test (test size = 0.3). My model has 1 input layer, 5 hidden layers, and 1 output layer. I used ReLU for the input and hidden layers, and I used the sigmoid for the output layer.
During the compilation of the model, I used stochastic gradient descent (SGD) as the optimizer and the mean squared logarithmic error for the loss. I used mini-batch gradient descend (batch size = 4) for the training.
I am trying to calculate the accuracy on the test set I created previously.
The model evaluation for train set is about: 0.07 (loss) 0.76 (accuracy).
The model evaluation for test set is about: 0.07 (loss) 0.74 (accuracy).
Firstly, I would like to know if this is a good value for a model. Is the accuracy too small?
Plus, I would like to know if there's a way to improve accuracy based on my model.
I am trying to work on a project, so I was wondering if these values are acceptable.