AI-Augmented Detection of Cardiovascular Disorders: A Machine Learning Paradigm

Cardiovascular disease (CVD) is still among the top causes of death and permanent disability, making it a pressing international health concern. Because of its complex character, it requires sophisticated diagnostic tools that can make accurate and timely predictions. In response to this growing need, the integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies has accelerated the advancement of intelligent systems for cardiac health monitoring. This work introduces a robust machine learning (ML)-driven framework aimed at enhancing the diagnosis and prediction of cardiovascular diseases. The suggested method uses a broad range of classification techniques to address issues with data heterogeneity and the restricted generalizability of traditional machine learning models by utilizing IoT-generated health data. A high prediction accuracy of almost 98.70% was shown in experimental validation using the UCI Heart Disease dataset, outperforming conventional methods. A thorough analysis using metrics like precision, recall, and F1-score highlights the model's efficacy. Additionally, this study highlights how deep learning architectures, especially artificial neural networks, can perform better when large-scale, multi-institution medical datasets are integrated. The results significantly boost tailored healthcare delivery and AI-enabled cardiovascular diagnostics.