FusedCNN-LSTM: A Software-Oriented Multimodal Deep Learning Framework for Intelligent Hypertension Risk Prediction

Hypertension, a life-threatening global health challenge, requires early detection to prevent severe cardiovascular complications. Fundus imaging reveals microvascular alterations, yet conventional diagnosis often misses subtle early changes. This study introduces a multimodal deep learning framework that integrates clinical data, fundus images, and demographic features to improve hypertension prediction. Unlike single-modality approaches, our method captures complementary risk factors from both structured and unstructured data. We evaluate machine learning and deep learning models on clinical data, confirming DL's superior accuracy. For fundus images alone, a CNN achieves 74.44% accuracy, highlighting the limitations of unimodal image analysis. To overcome this, we propose a fused CNN-LSTM architecture that models both spatial biomarkers and temporal clinical trends. The framework achieves robust performance, with an overall accuracy of 98% and minimal variation across datasets. Implemented in TensorFlow/Keras, the system adopts a modular, software-oriented design, ensuring flexibility, ease of maintenance, and seamless integration into clinical workflows. This holistic approach enables timely intervention, improves patient outcomes, and reduces healthcare burdens.