Web Application Based on MachineLearning for Diabetes Detection usingMicrostrip Resonator and Streamlit.

Globally, the number of people living with diabetes is estimated to continue to increase. Blood needs to be drawn from the body to measure glucose. Invasive methods carry the risk of infection. Estimating the device using non-invasive methods can reduce the risks of invasive procedures. One way is to use machine learning. In this research, a machine learning-based web application was designed using the Artificial Neural Network (ANN) and K-nearest neighbor (KNN) methods to classify a person's diabetes type by adding additional features from the microstrip resonator. The data used is 1000 with 11 features and one output with two labels. The 11 features are gender, age, blood sugar levels, smoking habits, family history of diabetes, height, weight, Body Mass Index (BMI), frequency, return loss, and bandwidth. In the output, two labels are used, namely diabetes and non-diabetes. ANN and KNN both provide high accuracy above 90%. ANN delivers an accuracy of 99.3%, and KNN provides an accuracy of 99.67%. This model will be embedded in web applications created using Streamlit. It is hoped that this web application can simplify and speed up the diagnosis of diabetes so that the disease can be treated quickly and precisely from an early age. This is expected to minimize the dangerous effects of diabetes due to delays in treatment.