Abstract
Heart disease remains a leading cause of death worldwide, emphasizing the critical need for early diagnosis to enable effective treatment and management. Decision support systems (DSS) have the potential to significantly enhance the accuracy and efficiency of heart disease diagnosis. An advanced DSS designed for early diagnosis can provide healthcare professionals with essential information, expert advice, and treatment recommendations based on comprehensive patient data. By gathering information from various sources including medical history, risk factors, and test results, the DSS analyzes and processes the data using established diagnostic criteria and the latest medical research. The DSS then generates a diagnosis or a list of possible diagnoses, along with appropriate treatment recommendations, empowering healthcare professionals to make well-informed decisions and deliver more effective patient care. Incorporating machine learning algorithms into the DSS can further enhance its accuracy and efficiency. By training the DSS on extensive patient datasets, it can identify patterns and make predictions based on new patient data, ultimately leading to improved decision-making and better patient outcomes. In conclusion, the utilization of a DSS for heart disease diagnosis holds the potential to revolutionize the field by providing healthcare professionals with vital information, expert guidance, and treatment recommendations, thereby enhancing the accuracy, efficiency, and overall outcomes of heart disease diagnosis and management.