Hardware Accelerator Enhanced Multi-Label Classification of Cardiovascular Disorders Through Hybrid Shallow Neural Network

Cardiovascular diseases remain one of the most critical medical conditions worldwide, often resulting in severe complications that demand early and precise diagnosis. While previous studies have predominantly addressed cardiovascular disease prediction using single-label classification models, such approaches are insufficient to capture the multi-label and interdependent nature of myocardial complications. Motivated by these limitations, this research proposes a novel framework that leverages a Hybrid Shallow Neural Network (HSNN) architecture, designed to balance model complexity and computational efficiency. Additionally, a new feature selection algorithm, termed Multi-label Gini Importance (MGI), is introduced, which thoroughly evaluates and selects the most relevant features across all labels by employing a Gini-impurity-based mechanism. To further enhance the integrity of the dataset, a K-nearest-neighbors-based imputation strategy is utilized for addressing missing values. Experimental evaluations show the superiority of the proposed methodology, demonstrating significant advancements over traditional machine learning algorithms and multi-label classification techniques. The proposed framework achieves an outstanding F1-score of 91.2% and a Hamming loss of 0.052. To further validate the practicality of the proposed approach, the HSNN model was implemented on an Altera Arria 10 GX FPGA platform, demonstrating its hardware efficiency and real-time processing capability. The hardware implementation achieves a maximum operating frequency of 210 MHz, a low inference latency of 1.85 μs, and a high throughput of 540,000 inferences per second, while consuming only 4.2 W of power. Additionally, the design maintains low resource utilization across logic elements, DSP blocks, and memory, confirming its scalability and efficiency for embedded deployment. These findings affirm the robustness, interpretability, and predictive efficiency of the proposed system, offering a substantial contribution toward the development of intelligent clinical decision-support mechanisms for cardiovascular disease diagnosis and prognosis.