Remote Monitoring for Early Detection of Diabetic Foot Ulcers-An Effective Analysis

Diabetes is a prevalent and serious health concern with significant economic burdens. Diabetic foot ulcers (DFUs) are a major complication, affecting 15-34% of diabetic individuals. These slow-healing wounds on the foot bottom significantly impact patients' well-being. This paper explores the use of a classification algorithm, specifically Decision Tree (DT) analysis, for the sensitive analysis of temperature data. DTs aim to predict the correct labels for new data points. In this context, the model is trained on a comprehensive dataset, validated, and tested before being used to predict the presence of DFUs in unseen patients based on temperature measurements. By analyzing temperature differences between similar regions and identifying elevations above a threshold linked to inflammation, the DT model can potentially aid in early DFU detection.