Prediction of Heart Disease using Data Mining Techniques

Objectives: The objective of our work is to analyse various data mining tools and techniques in health care domain that can be employed in prediction of heart disease system and their efficient diagnosis. Methods/Statistical Analysis: A heart disease prediction model, which implements data mining technique, can help the medical practitioners in detecting the heart disease status based on the patient’s clinical data. Data mining classification techniques for good decision making in the field of health care addressed are namely Decision trees, Naive Bayes, Neural Networks and Support Vector Machines. Hybridizing or combining any of these algorithms helps to make decisions quicker and more precise. Findings: Data mining is a powerful new technology for the extraction of hidden predictive and actionable information from large databases that can be used to gain deep and novel insights. Using advanced data mining techniques to excavate valuable information, has been considered as an activist approach to improve the quality and accuracy of healthcare service while lowering the healthcare cost and diagnosis time. Using this technique presence of heart disease can be predicted accurately. Using more input attributes such as controllable and uncontrollable risk factors, more accurate results could be achieved. Applications/Improvements: This method can be further expanded. It can use many of input attributes. Other data mining techniques are also be used for predication such as Clustering, Time series, Association rules. The unstructured data available in healthcare industry database can also be mined using text mining.