Table 2. Classification report of the machine learning algorithms for classifying neurological damage.
| Algorithm | Accuracy | Kappa | Variables |
|---|---|---|---|
| Random forest | 0.83 | 0.41 | MCHC, RDW-SD, Types of lead exposure, RDW-CV, Globulin, Indirect bilirubin, Total protein |
| Support vector machine | 0.82 | 0.37 | MCHC, RDW-SD, Globulin, RDW-CV, Types of lead exposure, Total bilirubin, WBC |
| Generalized linear model | 0.84 | 0.43 | MCHC, RDW-SD, RDW-CV, Globulin, Types of lead exposure, Indirect bilirubin, Total bilirubin |
Notes.
Abbreviations
- WBC
- white blood cell count
- MCHC
- mean corpuscular hemoglobin
- RDW
- red blood cell distribution width
- CV
- Coefficient of variation
- SD
- standard deviation