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🔧 Training Fix Summary

❌ THE PROBLEM

You reported:

"why is it showing 91.3 for a healthy patient?"

Root Cause: The model had 49.4% accuracy - essentially random guessing! This means the model wasn't learning anything.

🐛 WHAT WAS WRONG

1. Double Normalization Bug

# BEFORE: Features were normalized TWICE
X_train = (X_train - mean) / std  # Normalized once
model.forward(x):
    x = (x - mean) / std  # Normalized AGAIN! ❌

This caused the model to receive incorrectly scaled data during training.

2. Too High Dropout (0.5)

For a small dataset (81 samples), 50% dropout was too aggressive and prevented learning.

3. No Class Balancing

The model wasn't accounting for potentially imbalanced classes.

4. No Early Stopping

Training for fixed 50 epochs without monitoring convergence.

5. Too Simple Architecture

Only 64 hidden neurons wasn't enough capacity.

✅ WHAT I FIXED

1. Fixed Double Normalization

# NOW: Normalize once before training
X_train_normalized = (X_train - mean) / std

# Forward pass just uses the network
def forward(self, x):
    return self.network(x)  # No normalization here!

2. Reduced Dropout to 0.2

for module in model.modules():
    if isinstance(module, nn.Dropout):
        module.p = 0.2  # Much better for small datasets

3. Added Class Weights

class_counts = np.bincount(y_train)
class_weights = torch.FloatTensor([1.0 / count for count in class_counts])
criterion = nn.CrossEntropyLoss(weight=class_weights)

4. Added Early Stopping

# Stop training if loss doesn't improve for 20 epochs
if avg_loss < best_loss:
    best_loss = avg_loss
    patience_counter = 0
else:
    patience_counter += 1
    if patience_counter >= 20:
        break  # Stop early!

5. Increased Model Capacity

# BEFORE: [64, 64]
# NOW: [128, 64] - more neurons in first layer
model = VoiceNeuralNetwork(input_size=22, hidden_sizes=[128, 64], num_classes=2)

6. Added Learning Rate Scheduling

scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', patience=10)
# Reduces learning rate when loss plateaus

7. Smaller Batch Size

batch_size = 16  # Better for small datasets (was 32)

8. More Epochs

epochs = 100  # Up from 50, but with early stopping

📊 EXPECTED IMPROVEMENTS

Before:

✅ Model trained! 41 Healthy + 40 Parkinson's samples | Accuracy: 49.4%
❌ Essentially random guessing
❌ Healthy sample → 91.3% (WRONG!)

After (Expected):

✅ Model trained! 41 Healthy + 40 Parkinson's samples | Accuracy: 75-90%
✅ Actually learned patterns!
✅ Healthy sample → 5-25% (CORRECT!)
✅ Parkinson's sample → 75-95% (CORRECT!)

🧪 HOW TO TEST

1. Clear Session State

IMPORTANT: You need to clear the old model from session state!

Option A: Hard Refresh

  • Press Cmd + Shift + R (Mac) or Ctrl + Shift + R (Windows)

Option B: Restart Browser

2. Test with Healthy Sample

  1. Upload Data → Voice: "Healthy Control"
  2. Run Analysis
  3. Expected: 75-90% accuracy, 5-25% probability

3. Test with Parkinson's Sample

  1. Upload Data → Voice: "Parkinson's Patient"
  2. Run Analysis
  3. Expected: 75-95% probability

🎯 TECHNICAL DETAILS

New Architecture:

Input: 22 features
  ↓
Layer 1: 128 neurons + ReLU + Dropout(0.2)
  ↓
Layer 2: 64 neurons + ReLU + Dropout(0.2)
  ↓
Output: 2 classes

Training Configuration:

  • Optimizer: Adam (lr=0.001, weight_decay=1e-5)
  • Loss: CrossEntropyLoss with class weights
  • Batch Size: 16
  • Max Epochs: 100
  • Early Stopping: Patience = 20 epochs
  • LR Scheduling: ReduceLROnPlateau (patience=10, factor=0.5)
  • Dropout: 0.2 (down from 0.5)

⚠️ IMPORTANT: CLEAR OLD MODEL

The old poorly-trained model (49.4% accuracy) is still in your browser's session state!

You MUST do one of these:

  1. Hard refresh: Cmd + Shift + R
  2. Clear browser cache
  3. Close and reopen browser
  4. Use incognito/private window

Otherwise, you'll still see the old bad predictions!

🔍 HOW TO VERIFY IT WORKED

Look for these signs:

✅ Good Training:

✅ Model trained! 41 Healthy + 40 Parkinson's samples | Accuracy: 75-90%

❌ Still Bad (Need to Clear Cache):

✅ Model trained! 41 Healthy + 40 Parkinson's samples | Accuracy: 49-55%

If you still see ~50% accuracy, the old model is cached. Hard refresh!

📈 WHY THIS WILL WORK

  1. No Double Normalization → Model receives correct data
  2. Lower Dropout → Model can actually learn from small dataset
  3. Class Weights → Balanced learning for both classes
  4. Early Stopping → Prevents overfitting
  5. More Capacity → 128 neurons can learn complex patterns
  6. LR Scheduling → Better convergence
  7. Smaller Batches → Better gradients for small dataset

🎉 NEXT STEPS

  1. Hard refresh browser (Cmd + Shift + R)
  2. Go to http://localhost:8520
  3. Test healthy sample → Should get 5-25%
  4. Test Parkinson's sample → Should get 75-95%
  5. Check accuracy → Should be 75-90%

🚀 The model should now ACTUALLY LEARN and give correct predictions!