Analyze Important Features of PIMA Indian Database For Diabetes Prediction Using KNN

Abstract

Diabetes is a chronic, non-communicable disease, and a long-term health condition that affects how the body uses glucose, the type of sugar that gives energy. In Indonesia, diabetes ranks as the sixth highest cause of death, following conditions related to childbirth. In 2021, Indonesia has a total of 19.5 million diabetes patients, making it the fifth-highest in the world. Some machine learning research has used data from the PIDD (PIMA Indian Diabetes Dataset) to predict diabetes. In this research, in addition to prediction accuracy, data complexity is also important. This research analyzes important features in the PIMA Indian database using the KNN (k-nearest neighbor) method for classification. The results show that using KNN with k=22 value results in the highest accuracy of 83.12%. The analysis also found that the important features required by the KNN method to achieve high accuracy from the PIMA Indian database, in order of importance, are glucose, age, insulin, blood pressure, Body Mass Index, pregnancy, skin thickness, and diabetes pedigree function. However, when used in the KNN classification method, the diabetes pedigree function feature was found to be unnecessary, not relevant, and can be reduced.

Keywords

• diabetes prediction
• knn
• pidd
• importance features
• machine learning