Data-driven optimization strategies for enhanced cardiovascular risk assessment

Abstract

Cardiovascular diseases (CVD) remain a significant global health challenge, requiring the improvement of risk assessment methods using advanced data-driven optimization strategies. This study explores the field of ML by comparing the default implementations of Random Forest (RF) and Support Vector Machine (SVM) in the context of predicting cardiovascular risk. The study examines the potential enhancements achieved by optimizing these models using Particle Swarm Optimization (PSO), a metaheuristic algorithm known for its capacity to improve complex problem-solving. The comparative analysis assesses the predictive accuracy of default RF and SVM models in identifying cardiovascular risk factors. Subsequently, a PSOtechnique is employed to optimize the parameters of both RF and SVM models, with the objective of maximizing their predictive capabilities. Remarkably, the Random Forest model, when enhanced with PSO stands out as the most precise, attaining an impressive accuracy rate of 97.4%. To strengthen the analytical process, the research includes a thorough exploratory data analysis (EDA) phase. This involves a thorough analysis of the dataset to reveal hidden patterns, correlations, and possible variables that may cause confusion. The knowledge acquired from EDA not only enhances the optimization process but also facilitates a more profound comprehension of the complex interconnections among different cardiovascular risk factors. The study highlights the crucial significance of data pre-processing in guaranteeing the dependability and resilience of the models. The application of thorough techniques for handling missing data, outliers, and normalization significantly improves the quality of input data, thereby enhancing the performance of ML models. The utilization of various methods such as comparative analysis, optimization with PSO, exploratory data analysis, and rigorous data pre-processing highlights the possibility of creating cardiovascular risk assessment tools that are both highly accurate and dependable. The results of this study add to the ongoing discussion on utilizing sophisticated data analysis techniques to enhance patient outcomes and modify healthcare interventions in the field of cardiovascular health.