Research Article Open Access

Classification of Heart Disease Using Cluster Based DT Learning

Senthilkumar Mohan1, Chandrsegar Thirumalai1 and Abdalah Rababah2
  • 1 VIT University, India
  • 2 United Arab Emirates University, United Arab Emirates


In the rural side, due to the absence of cardiovascular ailment centers, around 12 million people passing away worldwide reported by WHO. The principal purpose of coronary illness is a propensity of smoking. Our Cluster based disease Diagnosis (CDD) applies the ML classifiers to improve the prediction accuracy of cardiovascular diseases. For this we have taken a real Cleveland dataset from UCI. First, the ML performance is evaluated through all features. Then, the dataset is split through the class pairs through its distribution. From this class pair, the significant features are identified through entropy process. Through our CDD approach four significant features are identified from thirteen features. From this four features, the ML performance increases when compared to all other features. That is, in RF model the accuracy improves to 9.5%, SVM by 7.2% and DT model by 2.3%.

Journal of Computer Science
Volume 16 No. 1, 2020, 50-55


Submitted On: 13 April 2019 Published On: 10 January 2020

How to Cite: Mohan, S., Thirumalai, C. & Rababah, A. (2020). Classification of Heart Disease Using Cluster Based DT Learning. Journal of Computer Science, 16(1), 50-55.

  • 1 Citations



  • Classification
  • Machine Learning