Classification and diagnosis of diabetic with neural network algorithm learning vector quantizatin (LVQ)

Arnita; M.S. Sinaga; Elmanani

doi:10.1088/1742-6596/1188/1/012091

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Classification and diagnosis of diabetic with neural network algorithm learning vector quantizatin (LVQ)

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Arnita, M.S. Sinaga, Elmanani

2019 Journal of Physics: Conference Series Vol. 1188 Issue 1 Conference paper Cited by 1 SDG 3

Abstract

Determining the type of Diabetes Mellitus (DM) is very important to determine what treatment is suitable for a patient. Unfortunately patient information about what type of diabetes is often ignored, so the patient gets a wrong diagnosis. This study aims to build a classification model in determining a DM patient diagnosed with one type of DM, namely type 1 DM, type 2 DM, Gestational DM or special type DM. The indicators used in determining the classification for diagnosing patients are age, sex, blood pressure, levels of blood glucose, weight, and height. The classification method used is the Neural Network method with Learning Vector Quantization (LVQ) algorithm. Algorithm LVQ provides results 96% accuracy for training data with final epoch is 759 and 90% accuracy for testing data. © Published under licence by IOP Publishing Ltd.

Affiliations

Universitas Negeri Medan, Jalan Willem Iskandar Pasar v Medan, Indonesia

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