Diagnosing diabetes using neural networks on small mobile devices

Karan, Oğuz; Bayraktar, Canan; Gümüşkaya, Halûk; Karlık, Bekir

doi:10.1016/j.eswa.2011.06.046

Cited by 98 publications

(46 citation statements)

References 13 publications

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“…Nivel de acierto: 84.24%. Karan et al (2012) Diagnostica la diabetes mediante una red neuronal y un sistema computacional de cuidado de la salud, en un esquema cliente servidor. El sistema es entrenado con algunos parámetros similares a los manejados por este documento.…”

Section: Ganji Y Abadehunclassified

Sistema Bayesiano para la Predicción de la Diabetes

2017

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ResumenEn este trabajo se propone un sistema de clasificación Bayesiano para la identificación temprana de la diabetes Mellitus con base en el análisis de algunas variables tales como número de embarazos, presión arterial diastólica, espesor cutáneo del tríceps, índice de masa corporal, herencia y edad. La metodología propuesta define y entrena el sistema propuesto con base en muestras tomadas de pacientes diabéticos y no diabéticos. El sistema se validó con pacientes diferentes, manteniendo la misma proporción entre individuos diabéticos y no diabéticos. Finalmente, el número de aciertos y errores en la detección de esta enfermedad fue comparado contra un test especializado. Los resultados indican que, en el 87,69% de los casos, el clasificador bayesiano logra detectar correctamente esta enfermedad con base en las variables antes mencionadas. No obstante, cuando se agregó la variable "insulina en suero", el porcentaje aumentó al 98.46%. Palabras clave: clasificador bayesiano; diabetes; entrenamiento de un sistema; detección automatizada Bayesian System for Diabetes Prediction AbstractThis study presents a Bayesian system for the early identification of diabetes Mellitus based on the analysis of some variables such as number of pregnancies, diastolic blood pressure, triceps skin thickness, body mass index, heredity and age. The proposed methodology establishes and trains a Bayesian classification system, based on samples of diabetic and non-diabetic patients. The system was tested with different patients maintaining the same ratio between diabetics and non-diabetic individuals. Finally, to detect this disease, the number of hits and errors obtained by the Bayesian classifier was compared with a specialized test. The results indicate that, considering the aforementioned variables the disease was correctly detected by the system in 87.69% of the analyzed cases. However, when the variable serum insulin was included, this percentage increased up to 98.46%.

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Section: Ganji Y Abadehunclassified

Sistema Bayesiano para la Predicción de la Diabetes

2017

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“…A study cited in Nadeem et al (2015) mentions EEG signal sensing using block sparse Bayesian learning. Karan et al (2012) present an artificial neural networks based system for monitoring diabetic patients. HopkinsPD, a system to monitor patients suffering from Parkinson's disease has active and passive monitoring capabilities .…”

Section: Monitoring System For Brain Neurological System Related Dismentioning

confidence: 99%

Remote patient monitoring: a comprehensive study

Malasinghe

Ramzan

Dahal

2017

J Ambient Intell Human Comput

373

180

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“…The Backpropagation algorithm is an iterative gradient algorithm to decrease the root mean square error. Every layer connected to the previous layer [30,38]. In this study, two different multilayered perceptron (MLP) network architectures were used.…”

Section: Artificial Neural Network (Ann) Ann Is a Machinementioning

confidence: 99%

Quality Control of Olive Oils Using Machine Learning and Electronic Nose

Ordukaya

Karlık

2017

Journal of Food Quality

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The adulteration of olive oils can be detected with chemical test. This is very expensive and takes very long time. Thus, this study is focused on reducing both time and cost. For this purpose, the raw data has been collected from olive oils by using an e-nose from different regions in Balikesir in Turkey. This study presents two methods to analyze quality control of olive oils. In the first method, 32 inputs are applied to the classifiers directly. In the second, 32-input collected data are reduced to 8 inputs by Principal Component Analysis. These reduced data as 8 inputs are applied to the classifiers. Different machine learning classifiers such as Naïve Bayesian, -Nearest Neighbors ( -NN), Linear Discriminate Analysis (LDA), Decision Tree, Artificial Neural Networks (ANN), and Support Vector Machine (SVM) were used. Then performances of these classifiers were compared according to their accuracies.

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Diagnosing diabetes using neural networks on small mobile devices

Cited by 98 publications

References 13 publications

Sistema Bayesiano para la Predicción de la Diabetes

Sistema Bayesiano para la Predicción de la Diabetes

Remote patient monitoring: a comprehensive study

Quality Control of Olive Oils Using Machine Learning and Electronic Nose

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