Java-Based Diabetes Type 2 Prediction Tool for Better Diagnosis

Abstract

Background: The concept of classification of clinical data can be utilized in the development of an effective diagnosis system by taking the advantage of computational intelligence. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important problem in neural networks. Unfortunately, although several classification studies have been carried out with significant performance, many of the current methods often fail to reach out to patients. Graphical user interface-enabled tools need to be developed through which medical practitioners can simply enter the health profiles of their patients and receive an instant diabetes prediction with an acceptable degree of confidence. Methods: In this study, the neural network approach was used for a dataset of 768 persons from a Pima Indian population living near Phoenix, AZ. A neural network mixture of experts model was trained with these data using the expectation-minimization algorithm. Results: The mixture of experts method was used to train the algorithm with 97% accuracy. A graphical user interface was developed that would work in conjunction with the trained network to provide the output in a presentable format. Conclusions: This study provides a machine-implementable approach that can be used by physicians and patients to minimize the extent of error in diagnosis. The authors are hopeful that replication of results of this study in other populations may lead to improved diagnosis. Physicians can simply enter the health profile of patients and get the diagnosis for diabetes type 2.

Computational Intelligence-Based Diagnosis Tool for the Detection of Prediabetes and Type 2 Diabetes in India

“…Second, the algorithm used the mixture of experts approach that has shown significant improvements in the prediction problem. This study demonstrated that the algorithm reached and accuracy of 99.36% in the Indian dataset compared with 97% in an earlier study (using a Pima Indian diabetes dataset) [12].…”

“…1000 EM iterations and 20 hidden nodes reached an accuracy of 89.28% 3. 1100 EM iterations and 20 hidden nodes reached the overall best accuracy of 96.9% [12].…”

“…Artificial intelligence strategies have been extensively explored and tested on Pima Indian diabetes dataset from the USA [12,13]. However, it is surprising that even though India has the highest number of diabetes patients, such innovative strategies are relatively unexplored.…”

Diabetes mellitus altera a sinalização osteogênica e atrasa o processo de reparo ósseo após expansão rápida da maxila

“…Estes resultados confirmam a instalação W|ávâááûÉ | 84 do DM, validando o modelo de estudo de acordo com pesquisas recentes Hu et al, 2013;Du et al, 2014). Existem diversas formas de indução experimental do DM descritas na literatura, tais como: infecções virais, pancreatectomia parcial, uso de hormônios anti-insulínicos, uso excessivo de hidrocortisona (hormônio adenocorticotrófico), o aloxano e a STZ (Tominaga, 2007;Shankaracharya et al, 2012). Neste estudo, optou-se pelo uso da droga STZ.…”

Propuesta y evaluación de un sistema de telemedicina y de ayuda a la decisión para el cuidado de pacientes con diabetes gestacional

“…Los algoritmos de aprendizaje escogidos han sido el árbol de decisión C4.5 (Quinlan, J. Ross, 1993) y la red neuronal con la arquitectura perceptrón multicapa (MLP) (Haykin, S. S., 1994). La elección de estos dos algoritmos se debe a que ambos han sido probados amplia y satisfactoriamente en el dominio de la diabetes para diferentes propósitos, predicción de glucemias (Pérez-Gandía et al, 2010), diagnosis de retinopatía diabética (Bourouis et al, 2014), metabolismo de la intolerancia de la glucosa o identificación de DM2 (Möhlig et al, 2006;Hische et al, 2010;Shankaracharya et al, 2012;Upadhyaya, Farahmand and Baker-Demaray, 2013) o para identificar factores que influyan en el control diabético (Huang et al, 2007). Aunque todavía no habían sido aplicados a la clasificación de glucemias en intervalos de ingesta.…”