Research Article
A Machine Learning-Based Prediction Model for Preterm Birth in Rural India
Table 3
Maternal features associated with PTB.
| S. no. | Feature ID | Feature name |
| 1 | PID | Patient identification | 2 | WA | Woman age | 3 | LMP | Last menstrual period | 4 | EDD | Estimated delivery date | 5 | G | Gravida | 6 | P | Parity | 7 | A | Abortion | 8 | L | Living | 9 | EL | Educational level | 10 | H | Height | 11 | W | Weight | 12 | BMI | Body mass index | 13 | BP | Blood pressure | 14 | HB | Hemoglobin | 15 | ANC | Antenatal care visit | 16 | ADD | Actual delivery date | 17 | OH | Obstetric history | 18 | PCS | Previous caesarean section | 19 | GA | Gestational age | 20 | BW | Birth weight | 21 | GDM | Gestational diabetes mellitus | 22 | FHR | Fetal heart rate | 23 | MG | Multiple gestation | 24 | ND | Normal delivery | 25 | MH | Previous medical history | 26 | LBW | Low birth weight | 27 | ASPX | Asphyxia | 28 | HT | Hypertension | 29 | PE | Preeclampsia | 30 | LV | Live birth | 31 | SB | Still birth | 32 | OB | Obesity | 33 | AN | Anemia | 34 | TH | Thyroid | 35 | NS | Neonatal status | 36 | PTB | Preterm birth |
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