You are in:Home/Publications/ Hybrid System for Lymphatic Diseases Diagnosis. 2nd International Conference on Advances in Computing, Communications and Informatics (ICACCI-2013), August 22-25, 2013, Mysore, India, pp. 343 – 347. DOI: 10.1109/ICACCI.2013.6637195.

Dr. Assoc. Prof. Ahmad Taher Azar :: Publications:

Title:
Hybrid System for Lymphatic Diseases Diagnosis. 2nd International Conference on Advances in Computing, Communications and Informatics (ICACCI-2013), August 22-25, 2013, Mysore, India, pp. 343 – 347. DOI: 10.1109/ICACCI.2013.6637195.
Authors: Hanaa I. Elshazly, Ahmad Taher Azar, Aboul Ella Hassanien
Year: 2013
Keywords: Not Available
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Local/International: International
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Abstract:

Machine-learning techniques such as decision support systems (DSS) are of great help in various fields. Medicine is one of the fields that can benefit from the application of data mining and pattern recognition techniques. The evolution of computational intelligence can improve many areas in health care including diagnosis, prognosis, screening, etc. The multiclass classification problem is important in data mining applications. Medical datasets are characterized by high dimensionality. Feature selection is considered as the main process to improve classification performance, particularly with the curse of dimensionality. This paper presents a hybrid system that combines the genetic algorithm (GA) and random forest (RF) for diagnosing lymphatic diseases. The genetic algorithm is used as a feature selection technique for reducing the dimension of the lymphatic diseases dataset and RF is used as a classifier. The performance of the proposed GA-RF system is compared with that of other feature selection algorithms combined with RF classifier such as principal component analysis (PCA), ReliefF, Fisher, sequential forward floating search (SFFS), and the sequential backward floating search (SBFS). The sensitivity and specificity were evaluated to measure the prediction performance. The experiments performed show that GA-RF achieved a high classification accuracy of 92.2%. Moreover, a subset of six features using the GA is sufficient for obtaining the classification.

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