부산대학교 도서관

Implementing Artificial intelligence in healthcare, particularly for analyzing cardiovascular diseases (CVD), is critical in reducing mortality rates. It employs diverse computational intelligence approaches to address CVD-related challenges. Initially, statistical methods establish correlations between various risk factors and outcomes. Additionally, significance tests compare risk factors between those with and without CVD. Subsequently, a hybrid statistical approach identifies the most critical, relevant, and non-redundant risk factors for accurate CVD prediction. Machine learning techniques utilize two datasets, achieving high prediction accuracies of 92.2% and 92.5%. This research pioneers advanced statistical analysis, significant risk factor identification, and an in-depth understanding of their interrelationships, revolutionizing CVD diagnosis and prognosis.