Publication Details
Abstract
Cardiovascular disorders are still one of the biggest causes of death around the world, thus getting a diagnosis quickly is not only crucial, but it can also save lives. However, conventional diagnostic techniques may be time-consuming, costly, and reliant on specialized medical knowledge. This makes it much harder to discover problems early, especially in areas where there aren't many healthcare experts. In response, our initiative is using machine learning to make predicting the risk of heart disease faster and more accurate. Our algorithm looks at patterns in patient health data, like age, blood pressure, cholesterol levels, and other lifestyle factors, to assist doctors make decisions faster and based on evidence. This method not only speeds up the diagnosis process, but it also gives doctors a useful tool that they can use in their daily job to help people stay healthy. The idea is not to take the job of doctors, but to provide them a dependable, smart system that makes diagnoses more accurate and helps them prioritize patients who are at high risk so that they can get the care they need right away. In this project, we look at and evaluate several machine learning methods to find the best model. We also talk about how it could be used in the real world and how it could be improved in the future, such as by adding real-time data integration and expanding to additional chronic diseases.