Early detection of dengue disease using extreme learning machine
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 l...
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| Format: | Article |
| Language: | English |
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Insight - Indonesian Society for Knowledge and Human Development
2018
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| Online Access: | http://eprints.uthm.edu.my/4555/ http://eprints.uthm.edu.my/4555/1/AJ%202018%20%28790%29%20Early%20detection%20of%20dengue%20disease%20using%20extreme%20learning%20machine.pdf |
| _version_ | 1848888318669881344 |
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| author | Suhaeri, Suhaeri Mohd Nawi, Nazri Fathurahman, Muhamad |
| author_facet | Suhaeri, Suhaeri Mohd Nawi, Nazri Fathurahman, Muhamad |
| author_sort | Suhaeri, Suhaeri |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T20:08:23Z |
| format | Article |
| id | uthm-4555 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:08:23Z |
| publishDate | 2018 |
| publisher | Insight - Indonesian Society for Knowledge and Human Development |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-45552021-12-07T07:11:05Z http://eprints.uthm.edu.my/4555/ Early detection of dengue disease using extreme learning machine Suhaeri, Suhaeri Mohd Nawi, Nazri Fathurahman, Muhamad RB Pathology TA168 Systems engineering 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. Insight - Indonesian Society for Knowledge and Human Development 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/4555/1/AJ%202018%20%28790%29%20Early%20detection%20of%20dengue%20disease%20using%20extreme%20learning%20machine.pdf Suhaeri, Suhaeri and Mohd Nawi, Nazri and Fathurahman, Muhamad (2018) Early detection of dengue disease using extreme learning machine. International Journal on Advanced Science Engineering Information Technology, 8 (5). pp. 2219-2224. ISSN 2088-5334 |
| spellingShingle | RB Pathology TA168 Systems engineering Suhaeri, Suhaeri Mohd Nawi, Nazri Fathurahman, Muhamad Early detection of dengue disease using extreme learning machine |
| title | Early detection of dengue disease using extreme learning machine |
| title_full | Early detection of dengue disease using extreme learning machine |
| title_fullStr | Early detection of dengue disease using extreme learning machine |
| title_full_unstemmed | Early detection of dengue disease using extreme learning machine |
| title_short | Early detection of dengue disease using extreme learning machine |
| title_sort | early detection of dengue disease using extreme learning machine |
| topic | RB Pathology TA168 Systems engineering |
| url | http://eprints.uthm.edu.my/4555/ http://eprints.uthm.edu.my/4555/1/AJ%202018%20%28790%29%20Early%20detection%20of%20dengue%20disease%20using%20extreme%20learning%20machine.pdf |