Medical-Appointment
OpenML dataset with id 43617
Author name not available (Why is that?)
Full work available at URL: https://api.openml.org/data/v1/download/22102442/Medical-Appointment.arff
Upload date: 24 March 2022
Dataset Characteristics
Number of classes: 0
Number of features: 19 (numeric: 19, symbolic: 0 and in total binary: 0 )
Number of instances: 61,214
Number of instances with missing values: 0
Number of missing values: 0
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?
This page was built for dataset: Medical-Appointment
