Analysis of Patients' No-Show Appointment
The focus of this analysis was on Patient appointments in Brazil. The aim was to find out whether or not a patient showed up for their medical appointment. We analyzed a number of characteristics based on the information gathered in order to find out the possible reasons why a patient could miss their appointment.
The dataset for our analysis contains about 100k medical appointments in Brazil and can be found here.
The project was done using jupyter notebook. The programming tool used was python (specific libraries - Pandas, Numpy, Matplotlib, and Seaborn)
Click here to see the project
Research Questions:
The following questions were answered after our analysis
- Which gender has the most scheduled appointments?
- Which gender shows up more on their appointment days?
- Does age affect the chances of showing up?
- How does the gap in schedules (due_days) affect a patient's chance of showing up for their appointment?
- Do patients who receive sms show up more than those who do not?
- Do patients on scholarship show up for their appointments more than those who are not?
- Does a patient's handicap affect their chances of showing up?
- Do patients with a particular disease show up more than those without?
- Which months recorded the highest appointment, and what was the patients' turn-up for those months?
- What is the distribution of appointment schedules for each neighbourhood?