Algorithmic Insights into Predicting Hypertension Using Health Data in Cloud-Based Environments

This study presents a customized model built in Python by employing TensorFlow and sci-kit-learn, paving the way for the use of machine learning and cloud computing in medical image analysis. Tests with statistical data validate its superior diagnostic accuracy when compared to traditional models. Our method shows measurable improvements in workflow efficiency as well as clinical decision-making, even when looking past technical metrics. Potential efficiency gains are indicated by cloud computing's smooth integration into current workflows. Even with these achievements, there is always room for improvement and optimization. Improving the interpretability of the model alongside investigating federated learning for better privacy is among the recommendations. By recognizing the changing healthcare landscape together with opening the door for responsible and significant technology adoption, this research offers a revolutionary solution.