Classification of heart disease using cluster based DT learning

Senthilkumar Mohan, Chandrsegar Thirumalai, Abdalah Rababah

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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%.

Original languageEnglish
Pages (from-to)50-55
Number of pages6
JournalJournal of Computer Science
Issue number1
Publication statusPublished - 2020


  • Classification
  • Machine learning

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Artificial Intelligence


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