| AENLR | Adaptive elastic logistic net regression |
| ANFIS | Adaptive neuro-fuzzy inference system |
| ANN | Artificial neural network |
| ANOVA | Analysis of variance |
| AI | Artificial intelligence |
| BFG | Broyden–Fletcher–Goldfarb–Shanno quasi-Newton back propagation |
| BMI | Body mass index |
| BPML | Backpropagation multiple layer |
| BPNN | Back propagation in neural network |
| BR | Bayesian regularization |
| CVD | Cardiovascular disease |
| DF | Desirability function |
| DP | Differential probability |
| DT | Decision trees |
| ECG | Electrocardiogram |
| EEG | Electroencephalogram |
| FFB | Feed forward back propagation |
| FIS | Fuzzy inference system |
| HbA1c | Glycated hemoglobin |
| HDL | High density lipoprotein |
| kNN | k-nearest neighbor |
| LDA | Linear discriminant analysis |
| LDL | Low density lipoprotein |
| LM | Levenberg–Marquardt |
| MAE | Mean absolute error |
| MARS | Multivariate adaptive regression splines |
| MBE | Mean bias error |
| ME | Mean error |
| MESA | Multiple ethnic studies of atherosclerosis |
| MF | Membership functions |
| ML | Machine learning |
| NB | Naive Bayes |
| NSE | Nash–Sutcliffe efficiency |
| PMH | Past medical history |
| QDA | Quadratic discriminant analysis |
| RBC | Red blood cell |
| RBFNN | Radial basis functions neural networks |
| RSM | Response surface methodology |
| RMSE | Root of the mean square error |
| SCG | Scaled conjugate gradient |
| SD | Standard deviation |
| SF | Sensitivity factor |
| SS | Sum of squares |
| ST | Subject to |
| SVM | Support vector machines |
| SVR | Support vector regression |
| WHO | World Health Organization |