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Heart disease is one of the biggest causes for morbidity and mortality among the population of the world.  Prediction of cardiovascular disease is one of the important subjects in the section of clinical data analysis. Heart disease encompasses many diseases of the heart and blood pressure, heart attacks, angina pectoris (chest pain or discomfort caused by a reduced blood supply to the heart muscle), stroke and heart failure. Heart disease describes a condition that affects patient heart. In proposed work, Data mining techniques is used to predict the cardiac disease. Patient ID, Patient Name, Age, Gender, Delta Heart Rate and cpu-date are the attributes present in the dataset.  Clustering is an important data mining and descriptive task. It has been researched deeply by various researchers for diverse application areas and is applied in multiple working domains such as data classification and image processing. In proposed work Clustering algorithm, Balanced Iterative Reducing and clustering using Hierarchies (I-BIRCH) are used.  To establishing threshold value to the central point where its data formed a cluster dynamically, have verified the advantage on close ratio of the data of I-BIRCH algorithm of establishing threshold value dynamically through the experiment, put forward improve algorithm apply to in the loss analysis of Health Analysis System.

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Dr.N.Hemageetha, & V.Priya Dharshini. (2021). Prediction of cardiac disease using i-birch algorithm. International Journal of Intellectual Advancements and Research in Engineering Computations, 7(4), 3282–3288. Retrieved from