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Heart Disease Prediction Using Data Mining Approach

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dc.contributor.author Nadim, Mahimul Islam
dc.contributor.author Alam, Jane
dc.contributor.author Khanam, Humaira
dc.date.accessioned 2020-11-09T11:04:29Z
dc.date.available 2020-11-09T11:04:29Z
dc.date.issued 2019-11-25
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/5005
dc.description Heart disease usually refers to age-related structural changes in the heart, blood vessels, and veins, and kidney disease. High blood pressure is the main cause of the disease. Heart disease can occur at all ages. But older people are more at risk for the disease. Cholesterol levels generally increase with age and 80% of people with heart disease over the age of 65 die of heart disease. Again, the probability of having a stroke increases by twice the age of 50-55 years. As age increases, the elasticity of the arteries begins to decline, resulting in coronary artery disease. Men have more heart disease than women. It is mainly caused by smoking, cholesterol, hypertension, diabetes and many more. en_US
dc.description.abstract Most of the data in today's world is computerized, these are usually scattered and not properly utilized. Data mining works like a blind stick to utilize these data. As elsewhere, Bangladesh also contains heart disease which puts people at a significant risk of death. According to the World Health Organization (WHO), an estimated 17.9 million people died from cardiovascular diseases (CVD) [10]. Although there is a lot of data, we have little knowledge of decision making. We have identified the major sources of heart disease by reviewing these data and using data mining techniques. In this paper, the data mining system uses for medical sections such as Smoke, blood pressure, diabetes, systolic blood pressure, diastolic blood pressure like 15 attributes to find predict heart disease. This prediction predicts by some data mining algorithm namely Decision tree, Artificial Neural Network (ANN), SVM. The accuracy of these algorithms is (76.94%), (79.40%) and (84.12%). This work is carried out to track the performance of certain data mining techniques on certain selected attributes, as explained later. en_US
dc.language.iso en en_US
dc.publisher Daffodil International University en_US
dc.subject Data Mining en_US
dc.subject Coronary Heart Disease en_US
dc.title Heart Disease Prediction Using Data Mining Approach en_US
dc.type Other en_US

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