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Early Detection of Dengue Disease Using Extreme Learning Machine

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@article{IJASEIT5006,
   author = {Suhaeri Suhaeri and Nazri Mohd Nawi and Muhamad Fathurahman},
   title = {Early Detection of Dengue Disease Using Extreme Learning Machine},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {8},
   number = {5},
   year = {2018},
   pages = {2219--2224},
   keywords = {Machine learning, artificial neural networks, back propagation algorithm, dengue disease, extreme learning machine},
   abstract = {

Dengue disease is one of the serious and dangerous diseases that cause many mortality and spread in most area in Indonesia. There are about 201,885 cases had been reported in 2016 including 1,585 death cases. The availability of nowadays clinical data of Dengue disease can be used to train machine learning algorithm in order to automaticaly detect the present of Dengue disease of the patients.  This study will use the Extreme Learning Machine (ELM) method to classify the dengue by using the clinical data so that first aid can be given which can decrease some death risk. The back propagation neural network is one of the popular machine learning technique that capable of learning some complex relationship and had been used in many applications. However, back propagation neural network still suffers with some limitations such as slow convergence and easily getting stuck in local minima during training. Therefore, this research proposed an improved algorithm known as ELM which is an extension of Feed Forward Neural Network that utilize the Moore Penrose Pseudoinver matrix that gain the optimal weights of neural network architecture. The proposed ELM prevents several backpropagation issues by reducing the used of many parameters that solves the main drawbacks of Backpropagation algorithm that uses during the training phase of Neural Network. The result shows that the proposed ELM with selected clinical features can produce best generalization performance and can predict accurately with 96.94% accuracy. The proposed algorithm achieves better with faster convergence rate than the existing state-of-the-art hierarchical learning techniques. Therefore, the proposed ELM model can be considered as an alternative algorithm to apply for early detection of Dengue 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=5006},    doi = {10.18517/ijaseit.8.5.5006} }

EndNote

%A Suhaeri, Suhaeri
%A Mohd Nawi, Nazri
%A Fathurahman, Muhamad
%D 2018
%T Early Detection of Dengue Disease Using Extreme Learning Machine
%B 2018
%9 Machine learning, artificial neural networks, back propagation algorithm, dengue disease, extreme learning machine
%! Early Detection of Dengue Disease Using Extreme Learning Machine
%K Machine learning, artificial neural networks, back propagation algorithm, dengue disease, extreme learning machine
%X 

Dengue disease is one of the serious and dangerous diseases that cause many mortality and spread in most area in Indonesia. There are about 201,885 cases had been reported in 2016 including 1,585 death cases. The availability of nowadays clinical data of Dengue disease can be used to train machine learning algorithm in order to automaticaly detect the present of Dengue disease of the patients.  This study will use the Extreme Learning Machine (ELM) method to classify the dengue by using the clinical data so that first aid can be given which can decrease some death risk. The back propagation neural network is one of the popular machine learning technique that capable of learning some complex relationship and had been used in many applications. However, back propagation neural network still suffers with some limitations such as slow convergence and easily getting stuck in local minima during training. Therefore, this research proposed an improved algorithm known as ELM which is an extension of Feed Forward Neural Network that utilize the Moore Penrose Pseudoinver matrix that gain the optimal weights of neural network architecture. The proposed ELM prevents several backpropagation issues by reducing the used of many parameters that solves the main drawbacks of Backpropagation algorithm that uses during the training phase of Neural Network. The result shows that the proposed ELM with selected clinical features can produce best generalization performance and can predict accurately with 96.94% accuracy. The proposed algorithm achieves better with faster convergence rate than the existing state-of-the-art hierarchical learning techniques. Therefore, the proposed ELM model can be considered as an alternative algorithm to apply for early detection of Dengue disease.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=5006 %R doi:10.18517/ijaseit.8.5.5006 %J International Journal on Advanced Science, Engineering and Information Technology %V 8 %N 5 %@ 2088-5334

IEEE

Suhaeri Suhaeri,Nazri Mohd Nawi and Muhamad Fathurahman,"Early Detection of Dengue Disease Using Extreme Learning Machine," International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 5, pp. 2219-2224, 2018. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.8.5.5006.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Suhaeri, Suhaeri
AU  - Mohd Nawi, Nazri
AU  - Fathurahman, Muhamad
PY  - 2018
TI  - Early Detection of Dengue Disease Using Extreme Learning Machine
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 8 (2018) No. 5
Y2  - 2018
SP  - 2219
EP  - 2224
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Machine learning, artificial neural networks, back propagation algorithm, dengue disease, extreme learning machine
N2  - 

Dengue disease is one of the serious and dangerous diseases that cause many mortality and spread in most area in Indonesia. There are about 201,885 cases had been reported in 2016 including 1,585 death cases. The availability of nowadays clinical data of Dengue disease can be used to train machine learning algorithm in order to automaticaly detect the present of Dengue disease of the patients.  This study will use the Extreme Learning Machine (ELM) method to classify the dengue by using the clinical data so that first aid can be given which can decrease some death risk. The back propagation neural network is one of the popular machine learning technique that capable of learning some complex relationship and had been used in many applications. However, back propagation neural network still suffers with some limitations such as slow convergence and easily getting stuck in local minima during training. Therefore, this research proposed an improved algorithm known as ELM which is an extension of Feed Forward Neural Network that utilize the Moore Penrose Pseudoinver matrix that gain the optimal weights of neural network architecture. The proposed ELM prevents several backpropagation issues by reducing the used of many parameters that solves the main drawbacks of Backpropagation algorithm that uses during the training phase of Neural Network. The result shows that the proposed ELM with selected clinical features can produce best generalization performance and can predict accurately with 96.94% accuracy. The proposed algorithm achieves better with faster convergence rate than the existing state-of-the-art hierarchical learning techniques. Therefore, the proposed ELM model can be considered as an alternative algorithm to apply for early detection of Dengue disease.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=5006 DO - 10.18517/ijaseit.8.5.5006

RefWorks

RT Journal Article
ID 5006
A1 Suhaeri, Suhaeri
A1 Mohd Nawi, Nazri
A1 Fathurahman, Muhamad
T1 Early Detection of Dengue Disease Using Extreme Learning Machine
JF International Journal on Advanced Science, Engineering and Information Technology
VO 8
IS 5
YR 2018
SP 2219
OP 2224
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Machine learning, artificial neural networks, back propagation algorithm, dengue disease, extreme learning machine
AB 

Dengue disease is one of the serious and dangerous diseases that cause many mortality and spread in most area in Indonesia. There are about 201,885 cases had been reported in 2016 including 1,585 death cases. The availability of nowadays clinical data of Dengue disease can be used to train machine learning algorithm in order to automaticaly detect the present of Dengue disease of the patients.  This study will use the Extreme Learning Machine (ELM) method to classify the dengue by using the clinical data so that first aid can be given which can decrease some death risk. The back propagation neural network is one of the popular machine learning technique that capable of learning some complex relationship and had been used in many applications. However, back propagation neural network still suffers with some limitations such as slow convergence and easily getting stuck in local minima during training. Therefore, this research proposed an improved algorithm known as ELM which is an extension of Feed Forward Neural Network that utilize the Moore Penrose Pseudoinver matrix that gain the optimal weights of neural network architecture. The proposed ELM prevents several backpropagation issues by reducing the used of many parameters that solves the main drawbacks of Backpropagation algorithm that uses during the training phase of Neural Network. The result shows that the proposed ELM with selected clinical features can produce best generalization performance and can predict accurately with 96.94% accuracy. The proposed algorithm achieves better with faster convergence rate than the existing state-of-the-art hierarchical learning techniques. Therefore, the proposed ELM model can be considered as an alternative algorithm to apply for early detection of Dengue disease.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=5006 DO - 10.18517/ijaseit.8.5.5006