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

Arnita and Sinaga, M.S and Simamora, Elmanani (2018) Classification and diagnosis of diabetic with neural network algorithm learning vector quantizatin (LVQ). In: The Sixth Seminar Nasional Pendidikan Matematika Universitas Ahmad Dahlan 2018.lOP Con f. Series: Journal of Physics: Conf. Series 1188 (2019) 012099 lOP Publishing, 3 November 2018, Yogyakarta.

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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.

Item Type: Conference or Workshop Item (Paper)
Keywords: algorithm; learning vector quantizatin (LVQ); Classification and diagnosis
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA299 Analysis
Q Science > QA Mathematics > QA76 Computer software
R Medicine > R Medicine (General)
R Medicine > RJ Pediatrics
R Medicine > RJ Pediatrics > RJ50 Examination. Diagnosis
Divisions: Fakultas Matematika dan Ilmu Pengetahuan Alam > Matematika
Depositing User: Mrs Catur Dedek Khadijah
Date Deposited: 06 Mar 2023 07:49
Last Modified: 06 Mar 2023 07:49
URI: https://digilib.unimed.ac.id/id/eprint/51015

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