AI-Driven Predictive Modelling for Cardiovascular Disease Risk Assessment

Advanced predictive modelling is necessary for early identification and risk assessment of cardiovascular disease (CVD), as it continues to be a significant cause of death globally. This study compares established algorithms with novel ideas to investigate AI-driven predictive modelling tools for estimating the risk of cardiovascular disease. Support vector machines (SVM), random forests, and logistic regression are examples of machine learning methods used in the current models. Although these have demonstrated effectiveness in managing organised clinical data, they frequently struggle to include diverse data sources such genetic, lifestyle, and imaging data.To improve prediction accuracyand we present an approach that introduces deep learning models: recurrent neural networks (RNN) for time-series data and convolutional neural networks (CNN) for picture analysis. In order to take use of the advantages of both paradigms, we also investigate hybrid models that integrate neural networks with gradient boosting machines (GBM). The outcomes show that, in important categories like accuracy, sensitivity, and specificity, our suggested models perform better than conventional algorithms. Additional strengthening of the model resilience is achieved by integrating generative adversarial networks (GAN) for data augmentation. Clinical decision-making is improved by the comparative analysis, which shows a notable decrease in false positives and negatives.