Context
The No Show problem is one of the bigest on the health industry, about 30 of the patient fail theirs appointments.
Content
61K points, from 2017.01.01 to 2017.04.30 and 19 features to work with
Data Dictionary
especialidad : what kind of specialist is going to. Ie dematologist, etc.
edad: Age
sexo: sex, 1: Male, 2: Female
reservamesd : discrete value for the month of the appointment, 1: Jan, 2: Feb
reservamesc : continue value for the month of the appointment, the formula is COS(2reservamesdPi/12)
reservadiad : day of the week for the appointment, 1: Mon 7: Sun
reservadiac : continous value for the day of the week, the formula is COS(2reservadiadPi/7)
reservahorad : discrete value for hour of the appointment
reservahorac : continous value for the hour of the appointment, the formula is COS(2reservahoradPi/24)
creacionmesd : discrete value for the month when the appointment was created
creacionmesc : continous value for the month when the appointment was created, the formula is COS(2creacionmesdPi/12)
creaciondiad : same as reservadiad, but considering the day when the appointment was created
creaciondiac : same as reservadiac, but considering the day when the appintment was created
creacionhorad : hour when the appointment was created
creacionhorac : continous value for the creacionhourd, the formula is COS(2creacionhoradPi/24)
latencia : number of days between the appointment and the date when it was created
canal : channel used for the creation of the apppointment, 1: call center, 2: Personal, 3: Web
tipo : type of appointment, 1: medical, 2: procedures
show : 0: no show, 1: show
Inspiration
Can we use it to predict if a patient is going to show up for his appointment?