| T2DM |
Type 2 diabetes mellitus |
| IoT |
Internet of Things |
| IDF |
International Diabetes Foundation |
| WHO |
World Health Organization |
| VOC |
Volatile Organic Compounds |
| MOS |
Metal-Oxide-Semiconductor |
| ML |
Machine Learning |
| BGL |
Blood glucose level |
| E-Nose |
Electronic Nose |
| DiabeticSense |
Non-invasive multi-sensor diabetes detection device |
| ADC |
Analog-to-digital converter |
| LP |
Liquefied petroleum |
| ppm |
parts per million |
| mL |
milli-Litre |
| abs |
absolute |
| max |
maximum |
| min |
minimum |
| mean |
mean or average |
| stdDev |
standard deviation |
| avg |
average |
| G-Boost |
Gradient Boosting |
| DT |
Decision Tree |
| KNNs |
K-Nearest Neighbours |
| ENet |
Elastic Net |
| SVMs |
Support Vector Machines |
| XG-Boost |
eXtreme Gradient Boosting |
| RF |
Random Forest |
| AUC |
Area Under the Curve |
| MeanAcc |
Mean Accuracy |
| SHAPs |
Shapley additive explanations |
| BP |
Blood Pressure |
| SPO |
Oxygen level in blood |
| FFT |
Fast Fourier Transform |
| SMOTE |
Synthetic Minority Oversampling TEchnique |
| MSE |
Mean Square Error |
| sqrt |
Square root |
| T |
Temperature |
| H |
Humidity |
| °C |
degree Celsius |