[PDF] Top 20 Application of Artificial Neural Network and Binary Logistic Regression in Detection of Diabetes Status

... to diabetes mellitus. The method used to diagnose diabetes mellitus is classical and followed the criteria of the World Health Organization ...of diabetes mellitus is persuasive after adjusting for ... See full document

... without diabetes, 10 current study included partici- pants with or without ...without diabetes were randomized selected from their health fi les of Fengyutan community and the sample size was twice of ... See full document

... the logistic regression model, the BP neural network model is not affected by the interactions be- tween variables and has nonlinear mapping abilities, self- learning and self-adaptive ... See full document

... The perceptron algorithm was trained for 123 iterations with a learning rate η = 1.0. The classification accuracy was 98.91\% as shown in Table 3. The decision boundary learnt by the classifier trained on 2D data is ... See full document

... Pearson's correlation coefficient was used to determine the relationship between BMI and BF% with the continuous explanatory variables. The association between the outcome variable overweight/ no overweight and ... See full document

... care network [11], exploring patterns and cost of care [12], developing dis- ease risk prediction model [13, 14], chronic disease sur- veillance [15], and comparing disease prevalence ... See full document

... multiple regression and ANN approaches were used to predict the thrust forces in the drilling of AISI 316 stainless steel ...a network with five hidden ... See full document

... the neural system-based standard incorporates a store of capacity and is maybe advancing to be heaps of right, shifted analysts utilized it for establishment insurance that is that the primary focus of this ... See full document

... methods, logistic regression (LR) and artificial neural network (ANN) applied to mine the historical outage data for power distribution fault cause classification, are ...and ... See full document

... like artificial neural networks, capable of acting as universal ...both neural networks, and fuzzy logic systems to approximate each other as ...Both neural networks and fuzzy logic systems do ... See full document

... of logistic regression and artificial neural network was compared for estimating the risk of breast cancer and they found a similar performance of the methods and suggested using both ... See full document

... Altman, Iwanicza‐Drozdowska, Laitinen and Suvas (2017) stated that the MDA model underperformed compared to market‐based models, but the model preforms well for short‐term distress prediction. Therefore, the MDA model ... See full document

... Port scan is an attempt to intrude the system usually via internet. An intruder tries to find out a vulnerable server reading on some port but does not do direct damage and treats a port scan as an attack due to its ... See full document

... set of patients with suspected GCA from multiple centers and develop LR and NN models and externally validate them. We chose the NN model with the lowest FN rate. On review of the Pubmed, Embase, and Google Scholar ... See full document

... Back-propagation (BP) algorithms are the most popular training algorithms that are widely used due to their simplicity and the application for training FF-NN (Kulluk, 2013). In FF-BP networks, which are considered ... See full document

... Neural Network: ANN was created to imitate how the neural system of the human brain works (Hertz et ...a network of nodes interconnected in ...the binary outcome (failing or ... See full document

... In the binary response model expressed above, the response variable is binary or dichotomous. An individual can take on one of the two possible values, denoted for convenience by 0 and 1. Observations of ... See full document

... wise regression analysis implementations were carried out in order to decrease the numbers of variable by selecting from the variable pool which was formed after a literature ... See full document

... of diabetes and hs-CRP had a more important role in the identification of individuals with type 2 diabetes, while in MLR model, family history of diabetes, TG and hs- CRP and in SVM model, the family ... See full document

... One of the problems facing the medical research is the prediction of concurrent affliction to two diseases according to some common risk factors. These diseases are considered as joint distribution of two or several ... See full document