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 Remove any irrelevant or duplicate entries.  The model is trained to minimize the cross-entropy loss
between the predicted probabilities and the true labels.
2. Feature Engineering:
 Extract relevant information from raw data. F. Performance Evaluation
 Accuracy: Measures the percentage of correct
 Create new features that will improve model predictions.
performance.
 Class-Wise Performance: Assesses precision, recall, and
3. Normalization or Scaling:
F1-score for each disease.
 Ensure all features have comparable ranges, improving
convergence of machine learning algorithms.  Confidence Scores: Analyzes the reliability of model
predictions.
 Comparative Analysis: Compares the hybrid model's
performance to single-modal ML or DL systems.
 Scalability and Resource Efficiency: Evaluates the
computational efficiency and adaptability of the model
to diverse datasets.
G. Result Analysis
1. Metrics Analysis
A. Prediction Accuracy: Achieved high overall accuracy,
demonstrating reliable performance.
B. Confidence Scores:
 Provided consistent confidence levels, indicating
robust predictions.
C. Probability Distribution:
 Balanced predicted probabilities across diseases,
reducing bias.
D. Per-Class Metrics:
 Precision, recall, and F1-scores highlighted strong
performance, visualized using a confusion matrix.
2. Comparative Analysis
A. Better Than Single-Modality Systems:
 Improved accuracy and diagnosis precision.
B. Resource Usage:
 Required reasonable resources, making it efficient
and practical.
Fig: Data PRE-Processing Steps
C. Scalability:
C. Data Splitting
 Handled larger datasets well, maintaining good
1. Training Set:
performance as data increased.
 Used to train the machine learning model.
3. Key Insights
 The model learns patterns and relationships from this
A. Visualization: Results visualized using graphs like ROC
data.
curves and confusion matrices.
2. Testing Set: B. Effectiveness and Scalability: Proved effective and
 Used to evaluate the model's performance on unseen scalable, offering reliable predictions for chronic
data.
diseases.
 Assesses how well the model generalizes to new cases.
H. Conclusion and Future Work
D. Model Building A. Objective: Summarize the findings and outline future
1. Data Preprocessing: research directions.
 Text: TF-IDF vectorization
B. Future Research:
 Image: ResNet-18 feature extraction
 Explore real-time implementations.
2. Feature Engineering: - Combine text and image features
 Integrate additional data modalities.
3. Model Architecture: - Hybrid neural network (PyTorch)
 Validate the approach in clinical environments.
E. Model Training:
 The model is trained using a variant of stochastic The process involves collecting raw patient data from
gradient descent (SGD) or another optimization various sources, including medical records, imaging, and
algorithm. clinical notes, followed by data pre-processing to clean,
normalize, and engineer features for improved model
 The model is trained on the combined feature matrix performance. The data is split into training and testing sets,
and the corresponding labels.
with the training set used to teach a hybrid neural network

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