Web-based Application for Diagnosis of Diabetes using Learning Vector Quantization (LVQ) - Juni Wijayanti Puspita; Kevin Jieventius Yanto; Andi Moh. Ridho Pettalolo; Moh. Ali Akbar Dg. Matona; Handayani Lilies (International Journal on Information and Communication Technology (IJoICT), 2024)
Diabetes is a chronic disease that causes the most deaths in the world. This disease can cause long-term complications that develop gradually, such as heart attacks, strokes, and problems with the kidneys, eyes, skin, and blood vessels. Therefore, early diagnosis of diabetes is crucial for patients to know their diabetes status. In this study, we designed a web-based application for diabetes diagnosis using Learning Vector Quantization (LVQ). The dataset was collected from Kaggle's Diabetes Dataset which contains eight attributes, namely pregnancy, glucose, blood pressure, insulin, skin thickness, BMI, diabetes lineage function, and age, with two classes, namely negative diabetes (healthy) and positive diabetes. The results show that the best accuracy is 73.1% with a learning rate of 0.001. These findings can help patients detect diabetes problems early.