Early Detection of Dengue Disease Using 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.

Leukaemia Identification based on Texture Analysis of Microscopic Peripheral Blood Images using Feed-Forward Neural Network

“…Gowthaman compares SVM with ELM. From several experiments, it is concluded that ELM is better than SVM in terms of results and learning speed [15].…”

“…Nemissi uses ELM for the classification of breast cancer with excellent results. Besides, ELM has also been used to solve problems such as forecasting the inflation rate in Indonesia [14], the detection of dengue fever [15], the diagnosis of breast cancer [16], and others showing results with an average accuracy rate above 90%. Gowthaman compares SVM with ELM.…”

Modified Gray-Level Haralick Texture Features for Early Detection of Diabetes Mellitus and High Cholesterol with Iris Image

“…The medical use of image textures has been reported in the previous studies. Examples are early detection of Alzheimer's disease [ 6 ], dengue fever [ 7 ], osteoarthritis [ 8 , 9 ], and lung cancer [ 10 , 11 ], while research on disease diagnosis based on iris image feature extraction is used to diagnose kidney disease [ 12 ] and heart disease [ 13 ], detect Alzheimer's [ 14 ], detect stomach disorders [ 15 ], and others.…”

Exploratory Study of Some Machine Learning Techniques to Classify the Patient Treatment

“…Various techniques have been carried out on medically related datasets for many purposes. Due to the broad scope of the medical and health fields, various research topics are found which ranges from diagnosis [3]- [5], diseases [3], [4], [6]- [11], patient's condition [12]- [17], prescription and medication [1], [18]- [21], to genomics [22]- [24].…”

“…Indonesia, compared to a very large population and a very large area of the country, the studies regarding the medical records or electronical health are very limited. Some of the studies which focus on the local context are published in [11] and [15]. The first article presents the study results of early dengue disease detection with the dataset captured from some public health (PUSKESMAS).…”