Alternating decision trees for early diagnosis of heart disease

M. A. Jabbar, B. L Deekshatulu, Priti Chndra
  • November 2014, Institute of Electrical & Electronics Engineers (IEEE)
  • DOI: 10.1109/cimca.2014.7057816

What is it about?

Recent survey shows that heart disease is a leading cause of death in India and in world wide. Significant life savings can be achieved, if a timely and cost effective clinical decision system is developed. Adverse reactions occur if a disease is not diagnosed properly. A clinical decision support system can assist health care professionals for early diagnosis of heart disease from patient’s medical data. Machine learning and modern data mining methods are useful for predicting and classifying heart disease. In this paper we wish to develop effective alternating decision tree approach for early diagnosis of heart disease. Alternating decision tree is a new type of classification rule. It is a generalization of decision trees, voted decision stumps and voted decision trees. We have applied our approach on heart disease patient records collected from various hospitals in Hyderabad. Optimization of features improves efficiency of earning algorithm. We used PCA to determine essential features of heart disease data. Experimental results show that our decision support system achieves high accuracy and proving its usefulness in the diagnosis of heart disease.

Why is it important?

Data mining has been widely used in health care application and in the diagnosis of heart disease. Decision tree is one of the important data mining techniques that is widely used in diagnosis of Heart Disease. Alternating decision trees are new representation for classification rule which are easy to implement, robust and interpretable. Applying ADTrees has shown improved accuracy in medical domain. This paper investigates applying computational intelligence approach, ADTree for early diagnosis of heart disease. Our proposed approach achieved an accuracy of 91.66%.This research systematically tested using full training set and 10 fold cross validation to identify most accurate method. This study shows a proof that applying AD Tree with boosting and PCA can enhance the accuracy in the diagnosis of heart disease. This type of research using ADTrees will play an important role to help health care professionals.

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http://dx.doi.org/10.1109/cimca.2014.7057816

The following have contributed to this page: Dr AKHIL JABBAR MEERJA