DETECTION OF CARDIOVASCULAR DISEASE USING MACHINE LEARNING ANDDEEP LEARNING

Abstract

Abstract — Effective prevention and diagnostic measures are crucial for cardiovascular diseases (CVDs), as they are one of the primary causes of death globally. Newly developed techniques for early identification, individualised treatment, and risk assessment of CVD include machine learning (ML) and deep learning (DL). Many facets of managing cardiovascular illness include the application of ML and DL approaches. The traditional risk factors for CVD, such as smoking, diabetes, hypertension, and hyperlipidaemia, are outlined in the first section of the study, along with how ML models can incorporate these factors to increase the accuracy of risk prediction. The usage of ML algorithms for the early identification of cardiovascular disease (CVD) is then investigated through the analysis of clinical imaging data, such as cardiac MRI scans, electrocardiograms (ECGs), and echocardiograms. We emphasise the potential of deep learning techniques, especially convolutional neural. DL and ML to improve CVD patients' treatment plans. These data-driven methods have great potential to improve clinical outcomes and save healthcare costs, as they can guide the selection of appropriate surgical procedures and anticipate individual reactions to pharmaceutical therapies. We also talk about issues that need to be resolved in order for these technologies to be widely used in clinical practice, like data heterogeneity, the interpretability of ML models, and regulatory concerns. Precision healthcare is gaining ground thanks to the introduction of ML and DL techniques into cardiovascular therapy. We can improve risk stratification, facilitate early disease detection, and customise interventions to the unique characteristics of each patient by utilising large-scale datasets and cutting-edge computational techniques. This will ultimately result in better management of cardiovascular diseases and better patient outcomes.