AI-Driven Privacy-Preserving Sparse Transformer for Secure Healthcare Data Modeling

Most AI models in healthcare are designed to deliver high prediction accuracy. But they often overlook patient privacy. Many attention-based models also face a major challenge. Their complexity grows rapidly, about O(n2). This makes it difficult to scale. To solve both problems, this study introduces a sparse transformer framework. It is designed to protect sensitive healthcare data during predictive modeling. The model uses a genetic algorithm to select the best features. It adds Laplace noise to categorical embeddings. This step helps to hide patient-specific details. An entropy-guided sparse attention mechanism filters out the noise. Focuses only on useful features. This reduces unnecessary computations. Tests on a lung cancer dataset show strong results. The model reached 98% precision and a high F1 score. It performed better than GRU LSTM RNN and BERT. At the same time, it kept patient data safe and used fewer resources.