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Yasin S, Muhammad
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Computer Science & IT

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Optimization of Diabetes Disease Classification Using Learning Vector Quantization Algorithm(LVQ) Sianipar, Eska Avelina; Yasin S, Muhammad
Journal of Computer Science and Informatics Engineering Vol 4 No 2 (2025): April
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v4i2.1121

Abstract

Diabetes is a chronic disease that attacks humans. Diabetes is caused by high levels of sugar in the human body. Diabetes attacks the metabolic function of the body where the body cannot digest or use high levels of sugar in the human body. Diabetes is classified as a dangerous chronic disease because it has a very fatal impact on humans, especially if complications of the disease have occurred. This study aims to develop a method for classifying diabetes using the Learning Vector Quantization (LVQ) method. Clinical data from patients diagnosed with diabetes and patients who are not diagnosed with diabetes will be analyzed to identify patterns related to this disease. Attribute data such as pregnancies, glucose, blood pressure, skin thickness, insulin, BMI, diabetes pedigree function and age will be used as variables in this analysis. The calculation results show that the Winning Class (Lowest value from the weighted results). So the classification results show an assessment to class 2 because the closest value is 70.002265497656 while the furthest value is 182.52975490778 in class 1
Co-Authors Sianipar, Eska Avelina