Smartphone-Integrated Sensor-Based DFU Risk Assessment Using CatBoost and Deep Neuro-Fuzzy Intelligence

Diabetic Foot Ulcer (DFU) is a serious and common complication of diabetes mellitus, which can lead to lower limb amputation if not identified and treated in its early stages. This study introduces an integrated and intelligent system designed for the early detection and severity classification of DFUs by combining sensor-driven data collection with machine learning techniques in a mobile application. The research is based on a dataset comprising both clinical features (D-1 to D-16) and key sensor-based readings gathered from 316 participants. After preprocessing and normalization, the clinical data undergoes feature selection using CatBoost, which filters out the five least impactful features while preserving all sensor data due to its diagnostic relevance. The refined dataset is then processed using a Deep Neuro-Fuzzy Network (DN-FN) to deliver real-time DFU severity predictions, categorized into Low, Mid, and High-risk levels. The solution is deployed through an intuitive smartphone interface, enabling users to input clinical data once and conduct periodic sensor-based tests—including vibration, pressure, and temperature readings. The mobile application interfaces with embedded hardware via Bluetooth and performs offline inference using a compact version of the trained model. The system is designed to offer both patients and healthcare professionals a practical and interpretable tool for continuous monitoring of foot health, with the ultimate goal of reducing the risk and impact of DFU complications.