Data mining is the process of discovering
interesting patterns and knowledge from mammoth size of
data. Heart disease or cardiovascular disease is the class of
diseases that involve the heart or blood vessels (arteries and
veins). Today most countries face high and increasing rates of
heart disease and it has become a leading cause of
debilitation and death worldwide. In many countries heart
disease is viewed as a "second epidemic" replacing infectious
diseases leading to the main cause of death. Making a
diagnosis of heart disease includes taking a complete medical
evaluation, history, physical examination and early diagnosis
of heart disease can help in reducing the rate of mortality
(Thaksin University, 2006). One of the best ways to diagnose
a heart disease is by using decision tree algorithm. Most
researchers have applied J48 Decision Tree based on Gain
Ratio and binary discretization. Gini Index and Information
Gain are two successful types of Decision Trees that are less
used in the diagnosis of heart disease. Some of the
discretization techniques like voting method and reduced
error pruning are known to produce more accurate Decision
Trees. This research work investigates the results after
applying a range of techniques to different types of Decision
Trees in order to get better performance in heart disease
diagnosis. To evaluate the performance of the alternative
Decision Trees the sensitivity, specificity, and accuracy are
calculated. This research work proposes a model that
performs better than J48 Decision Tree and Bagging
algorithm in the diagnosis of heart disease.
Published In:IJCSN Journal Volume 5, Issue 6
Date of Publication : December 2016
Pages : 885-892
Figures :01
Tables : 06
Mudasir Manzoor Kirmani : SKUAST-K, J&K, India.
Syed Immamul Ansarullah : MANUU, Hyderabad, India.
Data Mining, Decision Tree, Discretization, Heart
Disease
Decision Tree is one of the best data mining techniques
used in the diagnosis of heart disease; but compared to
other data mining algorithms its accuracy is not perfect.
This research work systematically tested decision tree type and voting to identify a more robust, more accurate
method. Applying voting shows increase in the accuracy
of different types of Decision Tree. Gini Index Decision
Tree can enhance the accuracy of the diagnosis of heart
disease.
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