Reliable Heart Disease Models: Enhancing Data Integrity and Traceability

Heart disease stands as a worldwide top killer so scientists seek to build dependable prognostic diagnostic and therapeutic models for cardiovascular diseases. This paper investigates how data integrity combined with traceability functions helps to improve the reliability of heart disease prediction models. This paper explores the present difficulties which stem from diverse data set types alongside machine learning method integration and diagnostic model validation methods for different ethnic groups. The article investigates how blockchain technology and space along with IoT systems along with advanced analytic techniques enhance the traceability and robustness of predictive models. This investigation demonstrates the creation of correct heart disease models while promoting interdisciplinary team effort and clear methods and firm data governance rules. The developed solutions will boost patient results through individualized care and supply essential information for public health tactics while handling healthcare and research ethical dilemmas.