A Data-Driven Approach in predicting Heart Disease Detection in earlier stage

In the healthcare field accumulation of vast amounts of data, known as big data, that contains valuable insights important for decision-making is available. The Data-driven approaches consistently proves their effectiveness by highlighting the pivotal roles of data analysis in error detection, valuable information identification, healthcare variables assumption and validation, and correlation establishment among health-related System. The proposed system Heart-Risk predict (HRP) serves as a data analysis tool by using statistical modeling techniques. The proposed novelistic model focuses on prediction of heart disease and risk factors using the k-means grouping algorithm by employing data analytics and visualization tools, which is further tested on publicly available datasets. Some of basic health related attributes like patient age, chest pain category, blood pressure, glucose levels and heart rate aid in early-stage chest pain type of determination. Through preprocessing methods and classifier-based evaluations, the current study showcases heart disease prediction accuracy at a higher level. Furthermore, the proposed system integrates the Tabu search mechanism and the K-means algorithm in addressing traditional method limitations. This integration enhances efficacy in handling large datasets by reducing computational complexity and give improvement in prediction accuracy. Thorough experimental validation depict system's efficacy and superiority over existing methods by offering a promising implications for early heart disease detection and proactive management, thus improvising public health outcomes.