A Machine Learning Approach to Cardiovascular Disease Prevention in Smart Healthcare

Cardiovascular diseases (CVDs) remain a prominent cause of death globally, but the incorporation of machine learning techniques into smart healthcare systems has demonstrated encouraging outcomes in detecting and preventing heart disease. Smart healthcare harnesses emerging technologies like wearable devices, electronic health records, and real-time data streaming to continuously monitor an individual's health indicators. By employing machine learning algorithms such as deep learning, support vector machines, and random forests, these extensive and diverse datasets can be analyzed to identify patterns, risk factors, and predictive models associated with CVDs. Additionally, this research emphasizes the significant role of predictive analytics in personalized medicine. Machine learning models can utilize patient-specific information to evaluate the likelihood of developing CVDs, enabling targeted interventions and customized treatment plans. Integrating patient preferences with clinical guidelines enhances decision-making processes that ultimately lead to enhanced patient outcomes.