Dimensionality Reduction using Genetic Algorithm for Improving Accuracy in Medical Diagnosis

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D. Asir Antony Gnana Singh 1,* E. Jebamalar Leavline 2 R. Priyanka 3 P. Padma Priya 3

1. Department of Computer Science and Engineering, Bharathidasan Institute of Technology, Anna University, Tiruchirappalli-620 024, India

2. Department of Electronics and Communication Engineering, Bharathidasan Institute of Technology, Anna University, Tiruchirappalli-620 024, India

3. Department of Information Technology, Bharathidasan Institute of Technology, Anna University, Tiruchirappalli-620 024, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2016.01.08

Received: 3 Apr. 2015 / Revised: 26 Jul. 2015 / Accepted: 22 Sep. 2015 / Published: 8 Jan. 2016

Index Terms

Attribute reduction, Naive Bayes classifier, genetic algorithm


The technological growth generates the massive data in all the fields. Classifying these high-dimensional data is a challenging task among the researchers. The high-dimensionality is reduced by a technique is known as attribute reduction or feature selection. This paper proposes a genetic algorithm (GA)-based features selection to improve the accuracy of medical data classification. The main purpose of the proposed method is to select the significant feature subset which gives the higher classification accuracy with the different classifiers. The proposed genetic algorithm-based feature selection removes the irrelevant features and selects the relevant features from original dataset in order to improve the performance of the classifiers in terms of time to build the model, reduced dimension and increased accuracy. The proposed method is implemented using MATLAB and tested using the medical dataset with various classifiers namely Na?ve Bayes, J48, and k-NN and it is evident that the proposed method outperforms other methods compared.

Cite This Paper

D. Asir Antony Gnana Singh, E. Jebamalar Leavline, R. Priyanka, P. Padma Priya, "Dimensionality Reduction using Genetic Algorithm for Improving Accuracy in Medical Diagnosis", International Journal of Intelligent Systems and Applications(IJISA), Vol.8, No.1, pp.67-73, 2016. DOI:10.5815/ijisa.2016.01.08


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