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.

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Background Hypospadias remains the most prevalent congenital abnormality in boys worldwide. However, the limited infrastructure and number of pediatric urologists capable of diagnosing and managing the condition hinder the management of hypospadias in Indonesia. The use of artificial intelligence and image recognition is thought to be beneficial in improving the management of hypospadias cases in Indonesia. Objective We aim to develop and validate a digital pattern recognition system and a mobile app based on an artificial neural network to determine various parameters of hypospadias. Methods Hypospadias and normal penis images from an age-matched database will be used to train the artificial neural network. Images of 3 aspects of the penis (ventral, dorsal, and lateral aspects, which include the glans, shaft, and scrotum) will be taken from each participant. The images will be labeled with the following hypospadias parameters: hypospadias status, meatal location, meatal shape, the quality of the urethral plate, glans diameter, and glans shape. The data will be uploaded to train the image recognition model. Intrarater and interrater analyses will be performed, using the test images provided to the algorithm. Results Our study is at the protocol development stage. A preliminary study regarding the system’s development and feasibility will start in December 2022. The results of our study are expected to be available by the end of 2023. Conclusions A digital pattern recognition system using an artificial neural network will be developed and designed to improve the diagnosis and management of patients with hypospadias, especially those residing in regions with limited infrastructure and health personnel. International Registered Report Identifier (IRRID) PRR1-10.2196/42853

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