| Related citation: | Kamasamudram Bhavya Sai,Rishi Raghu,Sai Surya Varshith Nukala,Jayashree Jayaraman,Vijayashree Jayaraman.Home-based Detection and Prediction of Diabetic Foot Ulcers at EarlyStage Using Sensor Technology and Supervised Learning[J].Journal of Harbin Institute Of Technology(New Series),2024,31(1):26-37.DOI:10.11916/j.issn.1005-9113.2023019. |
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| Author Name | Affiliation | | Kamasamudram Bhavya Sai | School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India | | Rishi Raghu | School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India | | Sai Surya Varshith Nukala | School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India | | Jayashree Jayaraman | School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India | | Vijayashree Jayaraman | School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India |
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| Abstract: |
| For years, foot ulcers linked with diabetes mellitus and neuropathy have significantly impacted diabetic patients health-related quality of life (HRQoL). Diabetes foot ulcers impact 15% of all diabetic patients at some point in their lives. The facilities and resources used for DFU detection and treatment are only available at hospitals and clinics, which results in the unavailability of feasible and timely detection at an early stage. This necessitates the development of an at-home DFU detection system that enables timely predictions and seamless communication with users, thereby preventing amputations due to neglect and severity. This paper proposes a feasible system consisting of three major modules: an IoT device that works to sense foot nodes to send vibrations onto a foot sole, a machine learning model based on supervised learning which predicts the level of severity of the DFU using four different classification techniques including XGBoost, K-SVM, Random Forest, and Decision tree, and a mobile application that acts as an interface between the sensors and the patient. Based on the severity levels, necessary steps for prevention, treatment, and medications are recommended via the application. |
| Key words: diabetic foot ulcer, podiatry, diabetes mellitus, healthcare, footcare, internet of things, machine learning, human-computer interaction |
| DOI：10.11916/j.issn.1005-9113.2023019 |
| Clc Number:TP391.5 |
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