Noninvasive Prototype for Type 2 Diabetes Detection
| Autor: | Luis Carlos Rodríguez Timaná, Adrián David Valencia Hernández, Javier Ferney Castillo García, Osamah Ibrahim Khalaf, Jesús Hamilton Ortiz |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Medicine (General)
Article Subject Biometrics Computer science Active learning (machine learning) Population Biomedical Engineering Health Informatics Type 2 diabetes Machine learning computer.software_genre Machine Learning Cohen's kappa R5-920 medicine Medical technology Humans R855-855.5 education Graphical user interface education.field_of_study business.industry LESS THAN 2 MINUTES Process (computing) medicine.disease Diabetes Mellitus Type 2 Surgery Artificial intelligence business computer Algorithms Research Article Biotechnology |
| Zdroj: | Journal of Healthcare Engineering, Vol 2021 (2021) Journal of Healthcare Engineering |
| ISSN: | 2040-2309 |
| Popis: | The present work demonstrates the design and implementation of a human-safe, portable, noninvasive device capable of predicting type 2 diabetes, using electrical bioimpedance and biometric features to train an artificial learning machine using an active learning algorithm based on population selection. In addition, there is an API with a graphical interface that allows the prediction and storage of data when the characteristics of the person are sent. The results obtained show an accuracy higher than 90% with statistical significance ( p |
| Databáze: | OpenAIRE |
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