Heart Disease Prediction System using Data Mining Method

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2017 by IJETT Journal
Volume-47 Number-6
Year of Publication : 2017
Authors : Keerthana T K
  10.14445/22315381/IJETT-V47P259

MLA 

Keerthana T K "Heart Disease Prediction System using Data Mining Method", International Journal of Engineering Trends and Technology (IJETT), V47(6),361-363 May 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Heart disease is most common in present era. The treatment cost of heart disease is not affordable by most of the patients. So we can reduce this problem by a Heart Disease Prediction System (HDPS).It is helpful for earlier diagnosis of heart disease. Data mining techniques are used for the construction of HDPS. In health care field some systems use large healthcare data in varied forms such as images, texts, charts and numbers .But this datas are hardly visited and are not mined. This problem can be avoided by introducing HDPS. This system would enhance medical care and it can also reduce the costs. The system can handle complex queries for detection of heart disease and thus help to make intelligent medical decisions. This paper proposes a HDPS based on three different data mining techniques. The various data mining methods used are Naive Bayes, Decision tree (J48), Random Forest and WEKA API. The system can predict the likelihood of patients getting a heart disease by using medical profiles such as age, sex, blood pressure, cholesterol and blood sugar. Also, the performance will be compared by calculation of confusion matrix. This can help to calculate accuracy, precision, and recall. The overall system provides high performance and better accuracy.

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Keywords
HDPS; WEKA; Random Forest; Naïve Bayes; J48.