Data Preprocessing:
Read the provided experimental data and concat them. Separate the data into features (Four_1, Four_2, Four_3, Four_4, etc) and labels (Patient group: Control, AD, MCI). Label Encoding:
Create a label encoding dictionary for the patient groups (Control, AD, MCI) to numerical values (e.g., 0, 1, 2). Map the patient groups to their corresponding numerical labels. Architecture:
Input Layer: The neural network starts with an input layer. It has 305 input units, which matches the number of features in your dataset. Hidden Layer: The first hidden layer has 64 neurons (units) and uses the ReLU activation function. This layer processes the input data and learns relevant patterns. Output Layer: The output layer has as many neurons as there are unique encoded labels in your dataset. This layer does not use an activation function explicitly because the loss function used (Sparse Categorical Cross-Entropy) already performs the necessary activation for multi-class classification. Hyperparameters:
optimizer: The chosen optimization algorithm is 'adam'. Adam (short for Adaptive Moment Estimation) is a popular optimization algorithm that adapts the learning rate during training. loss: The loss function is set to 'SparseCategoricalCrossentropy'. This is appropriate for multi-class classification problems with integer-encoded labels. metrics: During training, the model is also tracking the 'accuracy' metric to monitor how well it's performing. epochs: The number of training epochs is set to 5. One epoch means the model has seen the entire training dataset once. batch_size: The code doesn't explicitly set a batch size, so it defaults to the default value used by TensorFlow.