Refining Deep Learning Precision in Cardiovascular Risk Assessment: Leveraging Meta-Heuristic Optimization Techniques for Hyperparameter and Architecture Fine-Tuning

Abstract

Cardiovascular diseases (CVDs) are still the top cause of death in the world, so we need more improved ways to figure out who is at risk early on. Deep learning (DL) has appeared potential in this range, but its exactness depends on how well the hyperparameters and organize plan are tuned. This consider looks at how to move forward DL models for figuring out cardiovascular hazard by utilizing meta-heuristic optimization strategies. These strategies, which are based on common and developmental forms, give dependable ways to discover your way through the tremendous and complicated look spaces that come with setting hyperparameters and design. To induce the most excellent comes about from the DL show settings, we utilized a blend of hereditary calculations (GAs), molecule swarm optimization (PSO), and reenacted strengthening (SA). We needed to move forward the model's capacity to foresee and apply to other circumstances by utilizing these meta-heuristic strategies. This would offer assistance us get around the issues with standard framework and irregular look strategies. The test utilized in this think about had a part of diverse clinical and statistic components, which made beyond any doubt that the hazard appraisal show was total. The results of our tests appeared that the models worked much way better, with the most excellent profound learning plans being able to recognize cardiovascular hazard with higher exactness, affectability, and exactness. The meta-heuristic strategies were able to discover the leading hyperparameters, which decreased overfitting and made the show more solid over distinctive populace bunches. The comparison too appeared that combining these methods worked way better than utilizing fair one optimization strategy. This appears how valuable multi-strategy approaches can be in DL advancement. This think about appears how meta-heuristic optimization can totally alter the precision of DL in figuring out cardiovascular hazard. We got way better anticipated execution by fine-tuning hyperparameters and plan. This made CVD risk classification more accurate and useful. More research needs to be done on how to use the model in real-life clinical settings and on finding more meta-heuristic methods to make the model even more accurate and stable.