International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 01 | Jan -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1705 Heart Disease Prediction Using Data Mining Ajad Patel, Sonali Gandhi, Swetha Shetty,Prof. Bhanu Tekwani 1 Ajad Patel, Dept. Of Information Technology, Vidyalankar Institute Of Technology, Maharashtra, India 2 Sonali Gandhi, Dept. Of Information Technology, Vidyalankar Institute Of Technology, Maharashtra, India 3 Swetha Shetty, Dept. Of Information Technology, Vidyalankar Institute Of Technology, Maharashtra, India 4 Prof.Bhanu Tekwani, Dept. Of Information Technology, Vidyalankar Institute Of Technology, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract – Heart disease is a major life threatening disease that cause to death and it has a serious long term disability. There is wealth of data available within the health care system. However, there is lack of effective tools to discover hidden relationships and trends in data .Advanced data mining techniques can help remedial situations. This paper describes about a prototype using data mining techniques mainly Naïve Bayes and WAC (Weighted Associated Classifier). The dataset is composed of important factors such as age ,sex, diabetic, height, weight, blood pressure, cholesterol, fasting blood sugar, hypertension, disease. The system indicates whether patient had a risk of heart disease or not. Key Words: Data mining, Naïve Bayes, WAC, Prediction 1. INTRODUCTION It is a world known fact that heart is the most essential organ in human body if that organ gets affected then it also affects the other vital parts of the body. Data mining aids in healthcare to support for effective treatment, healthcare management, customer relation management, fraud and abuse detection and decision making. A major challenge facing healthcare organizations (hospitals, medical centers) is the provision of quality services at affordable costs. Quality service implies diagnosing patients correctly and administering treatments that are effective. Poor clinical decisions can lead to disastrous consequences which are therefore unacceptable. Hospitals must also minimize the cost of clinical tests. They can achieve these results by employing appropriate computer- based information and/or decision support systems. The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not Dzmineddz to discover hidden information for effective decision making. Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge rich data hidden in the database. This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patients. For instance it might now be possible for the physicians to compare diagnostic information of various patients with identical conditions. Likewise, physicians can also confirm their findings with the conformity of other physicians dealing with an identical case from all over the world. Medical diagnosis is considered as a significant yet intricate task that needs to be carried out precisely and efficiently. The automation of the same would be highly beneficial. 1.1 Data Mining Data Mining is about explaining the past and predicting the future by means of data analysis. Data mining is a multi-disciplinary multi-disciplinary field which combines statistics, machine learning, artificial intelligence and database technology. The value of data mining applications is often estimated to be very high. Many businesses have stored large amounts of data over years of operation, and data mining is able to extract very valuable knowledge from this data. The businesses are then able to leverage the extracted knowledge into more clients more sales, and greater profits. This is also true in the engineering and medical fields. Data mining predicts the future of modeling. Predictive modelling is a process by which a model is created to predict an outcome. If the outcome is categorical it is called categorical and if the outcome is numerical it is called regression. Descriptive modeling or clustering is assignment of observations into clusters so the observation of same cluster are similar 1.2 Heart disease in India Cardiovascular diseases (CVDs) have now become the leading cause of mortality in India. A quarter of all mortality is attributable to CVD. Ischemic heart disease and stroke are the predominant causes and are responsible for >80% of CVD deaths. The Global Burden of Disease study estimate of age- standardized CVD death rate of ʹ͹ʹ per ͳͲͲ ͲͲͲ