Neural Network from Scratch for Diabetes Prediction

Project Overview

This project implements a neural network from scratch using PyTorch to predict diabetes in patients based on the PIMA Indians Diabetes Dataset. The implementation focuses on understanding the mathematical foundations of neural networks through a single-layer linear model that achieves 75% accuracy on diabetes classification. The project demonstrates core machine learning concepts including data preprocessing, statistical analysis, gradient descent optimization, and cross-validation, making it an excellent educational resource for understanding how neural networks operate at a fundamental level.

Problem & Motivation

Healthcare professionals need reliable tools to assess diabetes risk based on readily available patient metrics. Traditional statistical methods may miss complex patterns in medical data, while black-box machine learning models lack interpretability crucial for medical decisions.

System Architecture

The system implements a simple yet effective linear classifier that multiplies normalized patient features by learned coefficients to produce diabetes risk predictions. The end-to-end flow consists of: data loading → statistical analysis → normalization → feature augmentation → k-fold cross-validation → gradient descent optimization → model evaluation. This streamlined approach prioritizes transparency and computational efficiency while maintaining clinical relevance.

Key modules include:

• Data preprocessing: Handles missing values, statistical analysis, and feature normalization
• Feature engineering: Applies Pareto analysis and data augmentation techniques
• Training pipeline: Implements k-fold cross-validation with early stopping
• Optimization engine: Uses Adam optimizer with learning rate scheduling
• Evaluation system: Provides accuracy metrics and model interpretation

Design Choices

Matrix operations over neural layers: Implemented direct matrix multiplication for transparency and computational efficiency, avoiding PyTorch’s high-level neural network abstractions.

Adam optimizer with weight decay: Selected for adaptive learning rates and built-in regularization to prevent overfitting on the small medical dataset.

K-fold cross-validation: Ensures robust model evaluation across different data splits, critical for medical applications with limited data.

Early stopping mechanism: Prevents overfitting by monitoring validation accuracy and halting training when no improvement occurs.

Binary cross-entropy loss: Chosen for its mathematical properties in binary classification and gradient stability.

Data augmentation with noise: Adds Gaussian noise to training samples to improve model robustness and generalization.

Technical Deep Dive

Data Preprocessing and Statistical Analysis

The preprocessing pipeline handles the PIMA Indians Diabetes Dataset through comprehensive statistical analysis and normalization.

# Building a Neural Network from Scratch for Diabetes Prediction.ipynb
# Data preprocessing and Pareto analysis
for col in df.columns:
    sorted_col = df[col].sort_values(ascending=False)
    cumulative_sum = sorted_col.cumsum()
    total_sum = sorted_col.sum()
    pareto_index = cumulative_sum.searchsorted(0.8 * total_sum) / len(sorted_col)
    print(f"{col}: Pareto index = {pareto_index}")

The system identifies data skewness using Pareto analysis, with insulin showing the highest skew (Pareto index = 0.29), indicating the need for special handling of long-tail distributions.

Feature Normalization and Standardization

StandardScaler normalization ensures all features contribute equally to the learning process, preventing scale-dependent features from dominating predictions.

# Building a Neural Network from Scratch for Diabetes Prediction.ipynb
# Standardize the dependent data columns
scaler = StandardScaler()
scaled_features = scaler.fit_transform(df.drop('Outcome', axis=1))
df_scaled = pd.DataFrame(scaled_features, columns=df.columns.drop('Outcome'))
df_scaled['Outcome'] = df['Outcome']

This preprocessing step is crucial for gradient descent convergence and prevents numerical instability during training.

Data Augmentation Strategy

The augmentation function introduces controlled noise to training samples, effectively doubling the dataset size while improving model robustness.

# Building a Neural Network from Scratch for Diabetes Prediction.ipynb
# Data augmentation function
def augment_data(features, labels):
    noise = torch.randn(features.shape) * 0.1
    augmented_features = features + noise
    return torch.cat([features, augmented_features]), torch.cat([labels, labels])

The 0.1 noise factor provides sufficient variation without corrupting the underlying data patterns, helping the model generalize to unseen patient data.

Optimization and Loss Computation

The core learning mechanism uses Adam optimization with binary cross-entropy loss, implementing the mathematical foundation of neural network training.

# Building a Neural Network from Scratch for Diabetes Prediction.ipynb
# Define the learning rate and optimizer
learning_rate = 0.1
optimizer = torch.optim.Adam([coeffs], lr=learning_rate, weight_decay=1e-5)
scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=5)

# Define the loss function
def calc_loss(coeffs, features, labels):
    preds = (features @ coeffs).squeeze()
    loss = torch.nn.functional.binary_cross_entropy_with_logits(preds, labels)
    return loss

The loss function combines linear prediction with sigmoid activation through the logits formulation, providing numerical stability and efficient gradient computation.

Cross-Validation Training Loop

The k-fold validation ensures robust model evaluation by training on multiple data splits and tracking best performance across folds.

# Building a Neural Network from Scratch for Diabetes Prediction.ipynb
for fold, (train_idx, val_idx) in enumerate(kf.split(t_features)):
    train_features, train_labels = t_features[train_idx], t_labels[train_idx]
    val_features, val_labels = t_features[val_idx], t_labels[val_idx]
    
    # Data augmentation
    train_features, train_labels = augment_data(train_features, train_labels)
    
    for epoch in range(num_epochs):
        # Batch processing with gradient descent
        permutation = torch.randperm(train_features.size()[0])
        for i in range(0, train_features.size()[0], batch_size):
            optimizer.zero_grad()
            indices = permutation[i:i+batch_size]
            batch_features, batch_labels = train_features[indices], train_labels[indices]
            
            train_loss = calc_loss(coeffs, batch_features, batch_labels)
            train_loss.backward()
            optimizer.step()

The training loop implements mini-batch gradient descent with random shuffling, ensuring efficient learning while maintaining convergence stability.

Training Pipeline

The model uses binary cross-entropy loss with Adam optimization, implementing a learning rate of 0.1 with weight decay regularization (1e-5). The training strategy employs 32-sample mini-batches with random permutation for each epoch, ensuring diverse gradient estimates. The ReduceLROnPlateau scheduler automatically reduces learning rate by 0.1 when validation loss plateaus for 5 consecutive epochs, adapting to training dynamics. Early stopping prevents overfitting by monitoring validation accuracy with a patience of 10 epochs.

# Building a Neural Network from Scratch for Diabetes Prediction.ipynb
# Early stopping mechanism
if val_accuracy > best_val_accuracy:
    best_val_accuracy = val_accuracy
    epochs_no_improve = 0
else:
    epochs_no_improve += 1
    if epochs_no_improve == patience:
        print(f"Early stopping triggered after {epoch+1} epochs")
        break

Inference & Performance

The model achieves 75% accuracy through direct matrix multiplication of normalized features with learned coefficients. Prediction generation uses sigmoid activation to convert linear outputs to probabilities, with a 0.5 threshold for binary classification.

# Building a Neural Network from Scratch for Diabetes Prediction.ipynb
# Manual prediction demonstration
first_patient = torch.tensor(df_scaled.drop('Outcome', axis=1).iloc[0].values, dtype=torch.float)
prediction = torch.sigmoid(first_patient @ coeffs).squeeze()
rounded_prediction = torch.round(prediction)
print(f"Sigmoid Value: {prediction}")
print(f"Model Prediction: {rounded_prediction}")
print(f"Actual Outcome: {df.iloc[0]['Outcome']}")

The inference process demonstrates model transparency by showing the complete prediction pipeline from raw patient data to final diabetes risk assessment, making it suitable for clinical interpretation.

What’s Next

Future enhancements include implementing multi-layer architectures for capturing non-linear relationships in patient data, exploring advanced feature engineering techniques for medical datasets, integrating ensemble methods for improved prediction confidence, and developing interpretability tools for clinical decision support. Additional work could focus on handling class imbalance through specialized loss functions, implementing mixed-precision training for computational efficiency, and creating a web-based interface for real-time diabetes risk assessment in clinical settings.

Last updated on August 24, 2025 at 12:16 PM EST. See Changelog