Classification of heart disease using multiple classifiers

Classification of heart disease using multiple classifiers

Marco Alfonse 

Faculty of Computers and Information Sciences, Ain Shams University, Cairo 11566, Egypt

Corresponding Author Email: 
marco@fcis.asu.edu.eg
Page: 
45-49
|
DOI: 
https://doi.org/10.18280/rces.050301
Received: 
20 August 2018
|
Accepted: 
28 September 2018
|
Published: 
30 September 2018
| Citation

OPEN ACCESS

Abstract: 

Heart disease is amongst the most widely recognized diseases in the world. This research aims to consolidate the precision of heart disease classification/diagnosis by developing a system depending on multiple classifiers. The proposed system contains two phases, which are the preprocessing phase and the classification phase. The preprocessing phase includes data cleaning, normalization and accounting for missing values. In the classification phase, multiple classifiers are used as an ensemble technique based on the Multilayer Perceptron (MLP), K-Nearest Neighbor (K-NN) and C4.5. A heart disease dataset, which contains four databases and gathered from the UCI machine learning repository, was used for experiments. The proposed classification system gives 99.4% classification precision according to 10-fold cross-validation technique. The outcome obtained from the proposed system shows that its performance is better than that of already reported classification systems.

Keywords: 

heart disease, classification, multilayer perceptron, K-Nearest Neighbor (K-NN), C4.5

1. Introduction
2. The Heart Disease Dataset
3. The Proposed Classification Approach
4. Results and Discussion
5. Conclusions
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