Error when I run the ASCVD Risk score for heart disease

Course Q&A AI For Medicine AI for Medical Prognosis
1

def ascvd(x_age,
x_cho,
x_hdl,
x_sbp,
x_smo,
x_dia,
verbose=False
):
“”"
Atherosclerotic Cardiovascular Disease
(ASCVD) Risk Estimator Plus
“”"

# Define the coefficients
b_age = 17.114
b_cho = 0.94
b_hdl = -18.92
b_age_hdl = 4.475
b_sbp = 27.82
b_age_sbp = -6.087
b_smo = 0.691
b_dia = 0.874

# Calculate the sum of the products of inputs and coefficients
sum_prod =  b_age * np.log(x_age) + \
            b_cho * np.log(x_cho) + \
            b_hdl * np.log(x_hdl) + \
            b_age_hdl * np.log(x_age) * np.log(x_hdl) +\
            b_sbp * np.log(x_sbp) +\
            b_age_sbp * np.log(x_age) * np.log(x_sbp) +\
            b_smo * x_smo + \
            b_dia * x_dia

if verbose:
    print(f"np.log(x_age):{np.log(x_age):.2f}")
    print(f"np.log(x_cho):{np.log(x_cho):.2f}")
    print(f"np.log(x_hdl):{np.log(x_hdl):.2f}")
    print(f"np.log(x_age) * np.log(x_hdl):{np.log(x_age) * np.log(x_hdl):.2f}")
    print(f"np.log(x_sbp): {np.log(x_sbp):2f}")
    print(f"np.log(x_age) * np.log(x_sbp): {np.log(x_age) * np.log(x_sbp):.2f}")
    print(f"sum_prod {sum_prod:.2f}")
    
# TODO: Risk Score = 1 - (0.9533^(e^(sum_prod - 86.61) ) )
risk_score = 1 - (0.9533^(e^(sum_prod - 86.61) ) )

return risk_score

tmp_risk_score = ascvd(x_age=55,
x_cho=213,
x_hdl=50,
x_sbp=120,
x_smo=0,
x_dia=0,
verbose=True
)
print(f"\npatient’s ascvd risk score is {tmp_risk_score:.2f}")

Could someone please help me figure out why I get the error message (name ‘e’ is not defined) below when I run the code cells above?

NameError Traceback (most recent call last)
in
5 x_smo=0,
6 x_dia=0,
----> 7 verbose=True
8 )
9 print(f"\npatient’s ascvd risk score is {tmp_risk_score:.2f}")

in ascvd(x_age, x_cho, x_hdl, x_sbp, x_smo, x_dia, verbose)
42
43 # TODO: Risk Score = 1 - (0.9533^(e^(sum_prod - 86.61) ) )
—> 44 risk_score = 1 - (0.9533^(e^(sum_prod - 86.61) ) )
45
46 return risk_score

NameError: name ‘e’ is not defined

2

For exponential you have used ‘e’ which doesn’t mean anything. Use np.exp()

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