Using data to determine the types of patients likely

Using data to determine the types of patients likely

Suppose we want to identify regular ‘no-show-ers’, based on recorded information, can we determine the characteristics of patients who miss appointments.

Hospitals usually require that patients make appointments when they want to see a doctor or specialist. However, we sometimes have situations where patients miss their appointments their appointments and do not give reasons.

Suppose we want to identify regular ‘no-show-ers’, based on recorded information, can we determine the characteristics of patients who miss appointments. Thereby suggesting alternatives for such patients and ensure that doctors do not waste time waiting for them.

In this article we will use the Missed Appointments datasetfrom Kaggle. It contains information about 100K medical appointments made by patients in Brazil, their characteristics and appointment information, and whether they attended the appointment or not (a no-show). A description of the features is given below:

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So, let us dive in….

Did we have more patients missing appointments?

We can see that we had fewer patients missing their appointments, approximately 22 319 of them out of a 100K appointments.

Our interest is in the patients who missed their appointments hence for the rest of this task we will filter the data and concentrate on those who did not show up for the appointments.

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