Research Article
Optimization of Tree-Based Machine Learning Models to Predict the Length of Hospital Stay Using Genetic Algorithm
Table 3
Statistical characteristics of variables in the studied sample.
| No. | Variable | n | Mean ± SD/percent |
| 1 | Age (year) | 200 | 63 ± 19 | 2 | Sex | | | 3 | Female | 100 | 50% | 4 | Male | 100 | 50% | 5 | Marital status | | | 6 | Married | 179 | 90% | 7 | Single | 21 | 11% | 8 | Main insurance type | | | 9 | Ordinary social security insurance (ordinary SSI) | 180 | 90% | 10 | Occupational social security insurance (occupational SSI) | 5 | 3% | 11 | Employee health insurance (employee HI) | 3 | 2% | 12 | Special social security insurance (special SSI) | 6 | 3% | 13 | Without insurance (without insurance) | 6 | 3% | 14 | Physician expertise level | | | 15 | General practitioner | 90 | 45% | 16 | Specialist | 107 | 54% | 17 | Subspecialty physician | 3 | 2% | 18 | Medical advice no | | 2 ± 3 | 19 | LOS (day) | | 5.6 ± 3.4 |
|
|