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Feature Selection Method using Genetic Algorithm for Medical Dataset

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@article{IJASEIT10226,
   author = {Neesha Jothi and Wahidah Husain and Nur’Aini Abdul Rashid and Sharifah Mashita Syed-Mohamad},
   title = {Feature Selection Method using Genetic Algorithm for Medical Dataset},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {9},
   number = {6},
   year = {2019},
   pages = {1907--1912},
   keywords = {data mining; data mining in healthcare; medical dataset; feature selection; genetic algorithm.},
   abstract = {

There is a massive amount of high dimensional data that is pervasive in the healthcare domain. Interpreting these data continues as a challenging problem and it is an active research area due to their nature of high dimensional and low sample size. These problems produce a significant challenge to the existing classification methods in achieving high accuracy. Therefore, a compelling feature selection method is important in this case to improve the correctly classify different diseases and consequently lead to help medical practitioners. The methodology for this paper is adapted from KDD method. In this work, a wrapper-based feature selection using the Genetic Algorithm (GA) is proposed and the classifier is based on Support Vector Machine (SVM). The proposed algorithms was tested on five medical datasets naming the Breast Cancer, Parkinson’s, Heart Disease, Statlog (Heart), and Hepatitis. The results obtained from this work, which apply GA as feature selection yielded competitive results on most of the datasets. The accuracies of the said datasets are as follows: Breast Cancer - 72.71%, Parkinson’s – 88.36%, Heart Disease – 86.73%, Statlog (Heart) – 85.48 %, and Hepatitis – 76.95%. This prediction method with GA as feature selection will help medical practitioners to make better diagnose with patient’s disease.  

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10226},    doi = {10.18517/ijaseit.9.6.10226} }

EndNote

%A Jothi, Neesha
%A Husain, Wahidah
%A Abdul Rashid, Nur’Aini
%A Syed-Mohamad, Sharifah Mashita
%D 2019
%T Feature Selection Method using Genetic Algorithm for Medical Dataset
%B 2019
%9 data mining; data mining in healthcare; medical dataset; feature selection; genetic algorithm.
%! Feature Selection Method using Genetic Algorithm for Medical Dataset
%K data mining; data mining in healthcare; medical dataset; feature selection; genetic algorithm.
%X 

There is a massive amount of high dimensional data that is pervasive in the healthcare domain. Interpreting these data continues as a challenging problem and it is an active research area due to their nature of high dimensional and low sample size. These problems produce a significant challenge to the existing classification methods in achieving high accuracy. Therefore, a compelling feature selection method is important in this case to improve the correctly classify different diseases and consequently lead to help medical practitioners. The methodology for this paper is adapted from KDD method. In this work, a wrapper-based feature selection using the Genetic Algorithm (GA) is proposed and the classifier is based on Support Vector Machine (SVM). The proposed algorithms was tested on five medical datasets naming the Breast Cancer, Parkinson’s, Heart Disease, Statlog (Heart), and Hepatitis. The results obtained from this work, which apply GA as feature selection yielded competitive results on most of the datasets. The accuracies of the said datasets are as follows: Breast Cancer - 72.71%, Parkinson’s – 88.36%, Heart Disease – 86.73%, Statlog (Heart) – 85.48 %, and Hepatitis – 76.95%. This prediction method with GA as feature selection will help medical practitioners to make better diagnose with patient’s disease.  

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10226 %R doi:10.18517/ijaseit.9.6.10226 %J International Journal on Advanced Science, Engineering and Information Technology %V 9 %N 6 %@ 2088-5334

IEEE

Neesha Jothi,Wahidah Husain,Nur’Aini Abdul Rashid and Sharifah Mashita Syed-Mohamad,"Feature Selection Method using Genetic Algorithm for Medical Dataset," International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 6, pp. 1907-1912, 2019. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.9.6.10226.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Jothi, Neesha
AU  - Husain, Wahidah
AU  - Abdul Rashid, Nur’Aini
AU  - Syed-Mohamad, Sharifah Mashita
PY  - 2019
TI  - Feature Selection Method using Genetic Algorithm for Medical Dataset
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 9 (2019) No. 6
Y2  - 2019
SP  - 1907
EP  - 1912
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - data mining; data mining in healthcare; medical dataset; feature selection; genetic algorithm.
N2  - 

There is a massive amount of high dimensional data that is pervasive in the healthcare domain. Interpreting these data continues as a challenging problem and it is an active research area due to their nature of high dimensional and low sample size. These problems produce a significant challenge to the existing classification methods in achieving high accuracy. Therefore, a compelling feature selection method is important in this case to improve the correctly classify different diseases and consequently lead to help medical practitioners. The methodology for this paper is adapted from KDD method. In this work, a wrapper-based feature selection using the Genetic Algorithm (GA) is proposed and the classifier is based on Support Vector Machine (SVM). The proposed algorithms was tested on five medical datasets naming the Breast Cancer, Parkinson’s, Heart Disease, Statlog (Heart), and Hepatitis. The results obtained from this work, which apply GA as feature selection yielded competitive results on most of the datasets. The accuracies of the said datasets are as follows: Breast Cancer - 72.71%, Parkinson’s – 88.36%, Heart Disease – 86.73%, Statlog (Heart) – 85.48 %, and Hepatitis – 76.95%. This prediction method with GA as feature selection will help medical practitioners to make better diagnose with patient’s disease.  

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10226 DO - 10.18517/ijaseit.9.6.10226

RefWorks

RT Journal Article
ID 10226
A1 Jothi, Neesha
A1 Husain, Wahidah
A1 Abdul Rashid, Nur’Aini
A1 Syed-Mohamad, Sharifah Mashita
T1 Feature Selection Method using Genetic Algorithm for Medical Dataset
JF International Journal on Advanced Science, Engineering and Information Technology
VO 9
IS 6
YR 2019
SP 1907
OP 1912
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 data mining; data mining in healthcare; medical dataset; feature selection; genetic algorithm.
AB 

There is a massive amount of high dimensional data that is pervasive in the healthcare domain. Interpreting these data continues as a challenging problem and it is an active research area due to their nature of high dimensional and low sample size. These problems produce a significant challenge to the existing classification methods in achieving high accuracy. Therefore, a compelling feature selection method is important in this case to improve the correctly classify different diseases and consequently lead to help medical practitioners. The methodology for this paper is adapted from KDD method. In this work, a wrapper-based feature selection using the Genetic Algorithm (GA) is proposed and the classifier is based on Support Vector Machine (SVM). The proposed algorithms was tested on five medical datasets naming the Breast Cancer, Parkinson’s, Heart Disease, Statlog (Heart), and Hepatitis. The results obtained from this work, which apply GA as feature selection yielded competitive results on most of the datasets. The accuracies of the said datasets are as follows: Breast Cancer - 72.71%, Parkinson’s – 88.36%, Heart Disease – 86.73%, Statlog (Heart) – 85.48 %, and Hepatitis – 76.95%. This prediction method with GA as feature selection will help medical practitioners to make better diagnose with patient’s disease.  

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10226 DO - 10.18517/ijaseit.9.6.10226