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In this work, we use recurrent neural network (RNN) to predict the medical examination data with missing parts. There often exist missing parts in medical examination data due to various human factors, for instance, because human subjects occasionally miss their annual examinations. Such missing parts make it hard to predict the future examination data by machines. Thus, imputation of the missing...
Healthcare sector today generate a large amount of complex data about death cause, disease diagnosis, electronic patient records, etc. In this paper prediction and the causes of death event is done using web mining techniques. In the proposed approach, large-scale digital histories are captured for duration of 18 years from news reports of Queensland Government archive to make real-time predictions...
A prediction of dengue fever cases by using a predictor of rainfall, the rain days, the house index, and the larva-free number has been done in Jember Regency. The evaluation was done by comparing and calculating the deviation value of the predicted number of cases, as a result of the prediction, to the number of actual cases. This prediction simulation of the number of dengue fever cases is using...
Picrorhiza Kurroa (Kutki) is the crop grown in India which we are analyzing here in Himalayan region. Farmers face a great loss of production as well as economic loss due to the disease in plants or may be pest attacks on immense level. So, they use pesticides and fertilizers for better crop growth but these chemicals harm the crops in excess quantity. And also inhaling of these chemicals leads to...
Bacillary dysentery is an important infectious disease caused by shigella dysenteriae. Here, we characterized the dynamic temporal trend of bacillary dysentery, and identified climate-related risk factors and their roles in bacillary dysentery transmission in Harbin city, China. A database is integrated monthly climate factors and incidence rates of Harbin city from 1986 to 1990. In this study, three...
We propose database-aware regression methods for extrapolation from few measurements in the context of quantitative prognosis. The idea is to leverage a database of patients with similar conditions to increase the effective number of samples when we train a predictive model. Applying the proposed method to a database of glaucoma patients, we were able to predict the disease condition at a future time...
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