Medical Appointment Data Analysis
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Analyze medical appointment data to uncover trends and insights. Learn how to handle and visualize healthcare data to improve patient scheduling, optimize operations, and predict trends that can streamline healthcare services.
At a Glance
In this notebook we will try to analyze why would some patients not show up for their medical appointment and whether there are reasons for that using the data we have.
A person makes a doctor’s appointment, receives all the instructions, and no-show. Who to blame?
We will try to find some correlation between the different attributes we have and whether the patient shows up or not.
The dataset we are going to use contains 110.527 medical appointments and its 14 associated variables ( PatientId, AppointmentID, Gender, ScheduledDay, AppointmentDay, Age, Neighbourhood, Scholarship, Hypertension, Diabetes, Alcoholism, Handcap’, SMS_received, No-show )
Questions to answer
- What is the percentage of no-shows?
- What factors are important for us to know in order to predict if a patient will show up for their scheduled appointment?
- Is the time gender related to whether a patient will show or not?
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- Are patients with scholarships more likely to miss their appointment?
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- Are patients who don’t receive SMS more likely to miss their appointment?
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- Is the time difference between the scheduling and appointment related to whether a patient will show?
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- Does age affect whether a patient will show up or not?
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- What is the percentage of patients missing their appointments for every neighborhood?
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