Development of Electrical Impedance Spectroscopy (EIS) Technique to Classify Diabetes Mellitus Disease Using Machine Learning with Backpropagation Method
DOI:
https://doi.org/10.48048/tis.2025.10309Keywords:
Backpropagation, Classification, Diabetes mellitus, Electrical impedance spectroscopy, Machine learningAbstract
Diabetes mellitus is an urgent challenge for global health. According to data from the International Diabetes Federation (IDF), the prevalence of diabetes has increased significantly in the last 5 years and is predicted to reach 700 million cases by 2045. Recently, a diabetes screening method based on the electrical properties of cells using the Electrical Impedance Spectroscopy (EIS) technique was proposed. In previous studies, the EIS technique was only used to identify cell and tissue damage but was not yet able to classify a disease such as diabetes. This study aims to develop the EIS technique so that it can be used to classify diabetes mellitus using machine learning with the backpropagation method. The data source was obtained from direct measurements in the laboratory using 90 mice (Mus musculus) that were made to suffer from diabetes mellitus. Mice were confirmed to have diabetes mellitus through a fasting blood sugar test (FBST) as a reference, then their cell electrical properties were measured using EIS. The measurement data will be made into a dataset for a machine learning model with the backpropagation training method consisting of input layers, hidden layers, and output layers. Paired data parameters used as input and output layers are frequency, phase, and impedance with blood glucose levels. The results of making a machine learning model to develop the EIS technique in classifying diabetes mellitus produced a fairly good performance index with an accuracy of 98.39 %, precision of 99 %, recall of 97 %, F1-score of 98 %, and specificity of 99 %. EIS development with the help of machine learning that utilizes the backpropagation method can be used to classify diabetes mellitus. The results of developing EIS using machine learning can be used to classify diabetes mellitus independently in a short time but have high accuracy.
HIGHLIGHTS
- Development of electrical impedance spectroscopy (EIS) technique using backpropagation method successfully differentiated healthy, pre-diabetic, and diabetic conditions very well with 98.39 % accuracy, 99 % precision, 97 % recall, 98 % F1-score and 99 % specificity.
- Measurement results show that the impedance value of the diabetic group is lower than the control group, which indicates changes in fluid distribution and vascular resistance due to hyperglycemia.
- Phase angle decreased significantly in the diabetic group, indicating disruption of cell membrane integrity due to oxidative stress and inflammation.
- The impedance pattern of the pre-diabetic group is more similar to a healthy group but experiences gradual changes as it approaches the diabetic condition.
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