A snake optimization algorithm-based feature selection framework for rapid detection of cardiovascular disease in its early stages

Cardiovascular disease (CVD) is a disorder that negatively affects the heart and blood vessels. Manual screening is time-consuming and prone to human error. As a result, assessors might rely on machine learning (ML) methods and automated feature selection. Feature selection aims to zero in on a dataset's most relevant and valuable subset of features. High dimensionality is addressed, complexity is reduced, and model efficiency is improved with this approach. Black-box optimization methods that take cues from nature have gained popularity to solve complex problems without resorting to formal mathematical formulations. More recently, algorithms that mimic the hunting behavior of snakes have evolved as a method for finding optimal or almost optimal solutions to difficult situations. The fundamental purpose of this research was to offer a novel framework for the analysis of cardiovascular disease (CVD) data called CVD-SO, which uses snake optimization (SO). Five machine learning methods are applied to pick and classify valid medical data efficiently. By incorporating machine learning and the SO algorithm into a single framework, we can create a CVD diagnostic model with unprecedented accuracy. The final output is a model that detects CVD with a remarkable 99.9% accuracy. These results considerably improve our comprehension of the medical data preparation procedure. The widespread burden of CVD-related disorders and the associated death rates can be alleviated, and mortality rates reduced if healthcare systems adopt this paradigm and actively combat CVD by facilitating early interventions.