Research Article
[Retracted] Efficient Prediction of Missed Clinical Appointment Using Machine Learning
| Name | Type | Range | Description |
| Date of birth | Input | mm/dd/yyyy | Date of birth of patient | Race | Input | Like Asian, African, white | Race of patient | Sex | Input | Male/female/other | Sex of patient | Civil status | Input | Single, married, divorced, separated, widowed | Civil status | Admit | Input | Textual format | Reason of admission | Date of appointment | Input | mm/dd/yyyy date | Date of appointment | Status of appointment | Input | Pending, closed, canceled | Status of appointment | Cancel date | Input | mm/dd/yyyy date | Canceling appointment date | Cancel reason | Input | No-show-up, death, rescheduled, out of city | Canceling appointment reason | Create time | Input | Time stamp format | Time at which database entry record was inserted | Modified time | Input | Time stamp format | Time at which database entry record was modified | Procedure type | Input | Like ultrasound, office visit see table | In which procedure patient booked appointment | Patient age | Generated | Numeric | Created by date of birth | Age range | Generated | From age category | Created by patient age feature | Create Appt difference | Generated | No of days | Created by taking difference of appointment date and create time | Appointment season | Generated | Month of year | Created with the help of appointment date | Cancel difference | Generated | No. of days | Created by taking difference of cancel date and appointment date |
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