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HYBRID ANN AND FIREWORKS ALGORITHM FOR REAL-TIME SOFTWARE RELIABILITY PREDICTION

Ivy Botchway Felix Larbi Aryeh Boniface Kayode Alese
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Developing Diabetes Disease Classification Model using Sequential Forward Selection Algorithm

by Emrana Kabir Hashi, Md. Shahid Uz Zaman, Md. Rokibul Hasan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 5
Year of Publication: 2017
Authors: Emrana Kabir Hashi, Md. Shahid Uz Zaman, Md. Rokibul Hasan
10.5120/ijca2017916018

Emrana Kabir Hashi, Md. Shahid Uz Zaman, Md. Rokibul Hasan . Developing Diabetes Disease Classification Model using Sequential Forward Selection Algorithm. International Journal of Computer Applications. 180, 5 ( Dec 2017), 1-6. DOI=10.5120/ijca2017916018

@article{ 10.5120/ijca2017916018,
author = { Emrana Kabir Hashi, Md. Shahid Uz Zaman, Md. Rokibul Hasan },
title = { Developing Diabetes Disease Classification Model using Sequential Forward Selection Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2017 },
volume = { 180 },
number = { 5 },
month = { Dec },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number5/28793-2017916018/ },
doi = { 10.5120/ijca2017916018 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:59:47.083915+05:30
%A Emrana Kabir Hashi
%A Md. Shahid Uz Zaman
%A Md. Rokibul Hasan
%T Developing Diabetes Disease Classification Model using Sequential Forward Selection Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 5
%P 1-6
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining techniques are being used extensively in healthcare sector to discover hidden pattern and relationship between patients’ record and their medical diagnosis dataset. In the concept of disease prediction, high classification accuracy can be obtained from accurately pre-processed and trained model. But existence of unimportant and irrelevant attributes in the training dataset may decrease the predictive accuracy and increase the time complexity in training phase. To increase the accuracy and efficiency, feature selection technique is frequently used in data mining. In this paper, a sequential forward selection based wrapper approach is proposed to select optimal and informative feature subset. It is known that diabetes mellitus is the most serious health problem and the complications lead to cause of death. So the aim of this research is to identify the significant attributes and classify diabetes dataset. The proposed approach is used to build the classifier models like Decision tree, K-Nearest Neighbor and Support Vector Machine produces the accuracies of 81.17%, 86.36% and 87.01% respectively. Finally, from results it is clear that the proposed model is performing better with high accuracy comparing the similar existing models. In the research, the Pima Indian diabetes dataset is used.

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Index Terms

Computer Science
Information Sciences

Keywords

Classification Feature Selection Wrapper Approach Feature selection SFS Pima Indian Diabetes Dataset C4.5 KNN SVM

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