Data Driven Approach for Heart Failure Analysis

Identifying individuals who may be at risk of developing heart failure is crucial in the healthcare industry. By applying preventative therapies and managing the condition, patient outcomes can be improved and the burden on healthcare systems can be reduced. Machine learning-based predictive models have shown promise in this regard, offering the potential for precise risk stratification and tailored care. The process of creating a heart failure prediction model involves several steps, including data collection, preprocessing, feature selection, model construction, evaluation, and clinical application. With the help of this technology, healthcare professionals and researchers can develop and validate heart failure prediction models, leading to better outcomes for patients.