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This paper reports on the design and implementation of a mobile diagnostic tool for diabetes. Diabetes is a chronic, often deadly, disease which occurs when the pancreas does not produce enough insulin or when the body cannot effectively use the produced insulin. Globally, at least 422 million people are affected by diabetes in 2014, and it account...
Citations
... In the year of 2030, the sufferers of DM in Indonesia will increase to be 21,3 million people, according to the Indonesian Ministy of Health. The increasing prevalence of DM is expected to result in complications, such as nouropathy and nefropathy, so that the burden on the health sector would be increasing as well (12,9). The number of DM cases that are not diagnosed before and only known when complications have occurred makes DM often referred to as the silent killer [13]. ...
... The increasing prevalence rate creates a burden on the health sector in Indonesia because it is feared that it will result in complications and cause other diseases such as cardiovascular and cerebral vascular disease, neuropathy and nephropathy. 3 However, when it is diagnosed, the disease has already transformed into complications. 4 In 2013, a total of 175 million people of the 382 million people with DM in the world were not diagnosed beforehand. ...
The purpose of this study is to develop an interactive service for people with DM and design a product specifically for the Android operating system. This study employs a research and development (R&D) design. The research phase consisted of a situation analysis, data collection, product design, expert validation, product revision, product tryout, and final revision. The research instrument was a questionnaire. Data analysis involved descriptive quantitative in percentage form. The results of the validation from the Android application experts, which focused on display quality, technical concerns, audio, and video quality, were found at 93.75% (very acceptable). In addition, the results of the validity assessment from the material experts, including general health experts and nutritionists, reached 98.08% (very acceptable) and 86.54% (very feasible), respectively. Based on the results of expert validation, the application design is categorized as very acceptable for development as a DM interactive service product.
... Four-layer neural network with eight input neurons, 10 hidden neurons for each of two hidden layers and one neuron for output layer is trained and 92% accuracy was obtained. Different from the others, a mobile application was realized in [11] for diabetes diagnosis. Type of diabetes and the possibility of developing diabetes in the future were determined by the developed system using blood glucose level data. ...
... Four-layer neural network with eight input neurons, 10 hidden neurons for each of two hidden layers and one neuron for output layer is trained and 92% accuracy was obtained. Different from the others, a mobile application was realized in [11] for diabetes diagnosis. Type of diabetes and the possibility of developing diabetes in the future were determined by the developed system using blood glucose level data. ...
Machine learning methods have been frequently used for the diagnosis of several diseases recently because of its reliability and convenience. In this paper, a comprehensive overview of the literature related to diabetes and diabetic retinopathy has been done and diagnosis of diabetic retinopathy disease is investigated. Artificial Neural Networks (ANN) method has been applied to the problem using Rapid Miner, a data mining tool. Some other methods have also adapted to the problem, but ANN based detection approach gave the best results. 88.52% sensitivity has been obtained using the features of Messidor dataset. Besides showing the success of ANN in diabetic retinopathy detection, this study also proved that Rapid Miner can be used effectively for the analysis of diabetic retinopathy.
