Learning Vector Quantization for Diabetes Data Classification with Chi-Square Feature Selection

9th Annual Basic Science International Conference 2019

自然科学（总论）

Putri, Nadisa Karina^1 ; Rustam, Zuherman^1 ; Sarwinda, Devvi^1

Department of Mathematics, Universitas Indonesia, Depok, Indonesia^1

关键词: Blood sugar levels;    Classification methods;    Data classification;    Diabetes mellitus;    Learning Vector Quantization;    Metabolic disorders;    Non-normality;    Performance measurements;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/546/5/052059/pdf
DOI  :  10.1088/1757-899X/546/5/052059

学科分类：自然科学（综合）

来源: IOP

【 摘 要 】

Diabetes mellitus or commonly referred as diabetes is a metabolic disorder caused by high blood sugar level and the pancreas does not produce insulin effectively. Diabetes can lead to relentless disease such as blindness, kidney failure, and heart attacks. Early detection is needed in order for the patients to prevent the disease being more severe. According to the non-normality and huge dataset in medical data, some researchers use classification methods to predict symptoms or diagnose patients. In this study, Learning Vector Quantization (LVQ) is used to classify the diabetes dataset with Chi-Square for feature selection. The result of the experiment shows that the best accuracy is achieved at 80% and 90% of the data training and the performance measurement, which are precision, recall, and f1 score are the highest when the model contains all the features in the dataset.