9. Tachycardia is a condition that causes the heart to beat faster than normal at rest. The occurrence of tachycardia can have serious implications including increased risk of stroke or sudden cardiac arrest.

An analytics consultant has been hired by a major hospital to build a predictive model that predicts the likelihood that a patient at a heart disease clinic will suffer from tachycardia in the month following a visit to the clinic. The hospital will use this model to make predictions for each patient when they visit the clinic and offer increased monitoring for those deemed to be at risk. The analytics consultant has generated an ABT to be used to train this model.17 The descriptive features in this dataset are defined as follows:

AGE: The patient’s age GENDER: The patient’s gender (male or female)

WEIGHT: The patient’s weight HEIGHT: The patient’s height BMI: The patient’s body mass index (BMI) which is calculated as where weight is measured in kilograms and height in meters.

SYS. B.P.: The patient’s systolic blood pressure DIA. B.P.: The patient’s diastolic blood pressure HEART RATE: The patient’s heart rate H.R. DIFF.: The difference between the patient’s heart rate at this visit and at their last visit to the clinic PREV. TACHY.: Has the patient suffered from tachycardia before?
TACHYCARDIA: Is the patient at high risk of suffering from tachycardia in the next month?
The following table contains an extract from this ABT—the full ABT contains 2,440 instances.

Discuss this data quality report in terms of the following:

a. Missing values

b. Irregular cardinality

c. Outliers

d. Feature distributions

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weight height