Context
A person makes a doctor appointment, receives all the instructions and no-show. Who to blame?
If this help you studying or working, please dont forget to upvote :). Reference to Joni Hoppen and Aquarela Advanced Analytics Aquarela
Greetings!
Content
110.527 medical appointments its 14 associated variables (characteristics). The most important one if the patient show-up or no-show to the appointment. Variable names are self-explanatory, if you have doubts, just let me know!
scholarship variable means this concept = https://en.wikipedia.org/wiki/Bolsa_FamC3ADlia
14 variables
Data Dictionary
01 - PatientId
Identification of a patient
02 - AppointmentID
Identification of each appointment
03 - Gender
Male or Female . Female is the greater proportion, woman takes way more care of they health in comparison to man.
04 - DataMarcacaoConsulta
The day of the actuall appointment, when they have to visit the doctor.
05 - DataAgendamento
The day someone called or registered the appointment, this is before appointment of course.
06 - Age
How old is the patient.
07 - Neighbourhood
Where the appointment takes place.
08 - Scholarship
True of False . Observation, this is a broad topic, consider reading this article https://en.wikipedia.org/wiki/Bolsa_FamC3ADlia
09 - Hipertension
True or False
10 - Diabetes
True or False
Alcoholism
True or False
Handcap
True or False
SMS_received
1 or more messages sent to the patient.
No-show
True or False.
Inspiration
What if that possible to predict someone to no-show an appointment?