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ClarkeErrorGrid.py
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ClarkeErrorGrid.py
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'''
CLARKE ERROR GRID ANALYSIS ClarkeErrorGrid.py
Need Matplotlib Pyplot
The Clarke Error Grid shows the differences between a blood glucose predictive measurement and a reference measurement,
and it shows the clinical significance of the differences between these values.
The x-axis corresponds to the reference value and the y-axis corresponds to the prediction.
The diagonal line shows the prediction value is the exact same as the reference value.
This grid is split into five zones. Zone A is defined as clinical accuracy while
zones C, D, and E are considered clinical error.
Zone A: Clinically Accurate
This zone holds the values that differ from the reference values no more than 20 percent
or the values in the hypoglycemic range (<70 mg/dl).
According to the literature, values in zone A are considered clinically accurate.
These values would lead to clinically correct treatment decisions.
Zone B: Clinically Acceptable
This zone holds values that differe more than 20 percent but would lead to
benign or no treatment based on assumptions.
Zone C: Overcorrecting
This zone leads to overcorrecting acceptable BG levels.
Zone D: Failure to Detect
This zone leads to failure to detect and treat errors in BG levels.
The actual BG levels are outside of the acceptable levels while the predictions
lie within the acceptable range
Zone E: Erroneous treatment
This zone leads to erroneous treatment because prediction values are opposite to
actual BG levels, and treatment would be opposite to what is recommended.
SYNTAX:
plot, zone = clarke_error_grid(ref_values, pred_values, title_string)
INPUT:
ref_values List of n reference values.
pred_values List of n prediciton values.
title_string String of the title.
OUTPUT:
plot The Clarke Error Grid Plot returned by the function.
Use this with plot.show()
zone List of values in each zone.
0=A, 1=B, 2=C, 3=D, 4=E
EXAMPLE:
plot, zone = clarke_error_grid(ref_values, pred_values, "00897741 Linear Regression")
plot.show()
References:
[1] Clarke, WL. (2005). "The Original Clarke Error Grid Analysis (EGA)."
Diabetes Technology and Therapeutics 7(5), pp. 776-779.
[2] Maran, A. et al. (2002). "Continuous Subcutaneous Glucose Monitoring in Diabetic
Patients" Diabetes Care, 25(2).
[3] Kovatchev, B.P. et al. (2004). "Evaluating the Accuracy of Continuous Glucose-
Monitoring Sensors" Diabetes Care, 27(8).
[4] Guevara, E. and Gonzalez, F. J. (2008). Prediction of Glucose Concentration by
Impedance Phase Measurements, in MEDICAL PHYSICS: Tenth Mexican
Symposium on Medical Physics, Mexico City, Mexico, vol. 1032, pp.
259261.
[5] Guevara, E. and Gonzalez, F. J. (2010). Joint optical-electrical technique for
noninvasive glucose monitoring, REVISTA MEXICANA DE FISICA, vol. 56,
no. 5, pp. 430434.
Made by:
Trevor Tsue
7/18/17
Based on the Matlab Clarke Error Grid Analysis File Version 1.2 by:
Edgar Guevara Codina
codina@REMOVETHIScactus.iico.uaslp.mx
March 29 2013
'''
import matplotlib.pyplot as plt
#This function takes in the reference values and the prediction values as lists and returns a list with each index corresponding to the total number
#of points within that zone (0=A, 1=B, 2=C, 3=D, 4=E) and the plot
def clarke_error_grid(ref_values, pred_values, title_string):
#Checking to see if the lengths of the reference and prediction arrays are the same
assert (len(ref_values) == len(pred_values)), "Unequal number of values (reference : {}) (prediction : {}).".format(len(ref_values), len(pred_values))
#Checks to see if the values are within the normal physiological range, otherwise it gives a warning
if max(ref_values) > 400 or max(pred_values) > 400:
print "Input Warning: the maximum reference value {} or the maximum prediction value {} exceeds the normal physiological range of glucose (<400 mg/dl).".format(max(ref_values), max(pred_values))
if min(ref_values) < 0 or min(pred_values) < 0:
print "Input Warning: the minimum reference value {} or the minimum prediction value {} is less than 0 mg/dl.".format(min(ref_values), min(pred_values))
#Clear plot
plt.clf()
#Set up plot
plt.scatter(ref_values, pred_values, marker='o', color='black', s=8)
plt.title(title_string + " Clarke Error Grid")
plt.xlabel("Reference Concentration (mg/dl)")
plt.ylabel("Prediction Concentration (mg/dl)")
plt.xticks([0, 50, 100, 150, 200, 250, 300, 350, 400])
plt.yticks([0, 50, 100, 150, 200, 250, 300, 350, 400])
plt.gca().set_facecolor('white')
#Set axes lengths
plt.gca().set_xlim([0, 400])
plt.gca().set_ylim([0, 400])
plt.gca().set_aspect((400)/(400))
#Plot zone lines
plt.plot([0,400], [0,400], ':', c='black') #Theoretical 45 regression line
plt.plot([0, 175/3], [70, 70], '-', c='black')
#plt.plot([175/3, 320], [70, 400], '-', c='black')
plt.plot([175/3, 400/1.2], [70, 400], '-', c='black') #Replace 320 with 400/1.2 because 100*(400 - 400/1.2)/(400/1.2) = 20% error
plt.plot([70, 70], [84, 400],'-', c='black')
plt.plot([0, 70], [180, 180], '-', c='black')
plt.plot([70, 290],[180, 400],'-', c='black')
# plt.plot([70, 70], [0, 175/3], '-', c='black')
plt.plot([70, 70], [0, 56], '-', c='black') #Replace 175.3 with 56 because 100*abs(56-70)/70) = 20% error
# plt.plot([70, 400],[175/3, 320],'-', c='black')
plt.plot([70, 400], [56, 320],'-', c='black')
plt.plot([180, 180], [0, 70], '-', c='black')
plt.plot([180, 400], [70, 70], '-', c='black')
plt.plot([240, 240], [70, 180],'-', c='black')
plt.plot([240, 400], [180, 180], '-', c='black')
plt.plot([130, 180], [0, 70], '-', c='black')
#Add zone titles
plt.text(30, 15, "A", fontsize=15)
plt.text(370, 260, "B", fontsize=15)
plt.text(280, 370, "B", fontsize=15)
plt.text(160, 370, "C", fontsize=15)
plt.text(160, 15, "C", fontsize=15)
plt.text(30, 140, "D", fontsize=15)
plt.text(370, 120, "D", fontsize=15)
plt.text(30, 370, "E", fontsize=15)
plt.text(370, 15, "E", fontsize=15)
#Statistics from the data
zone = [0] * 5
for i in range(len(ref_values)):
if (ref_values[i] <= 70 and pred_values[i] <= 70) or (pred_values[i] <= 1.2*ref_values[i] and pred_values[i] >= 0.8*ref_values[i]):
zone[0] += 1 #Zone A
elif (ref_values[i] >= 180 and pred_values[i] <= 70) or (ref_values[i] <= 70 and pred_values[i] >= 180):
zone[4] += 1 #Zone E
elif ((ref_values[i] >= 70 and ref_values[i] <= 290) and pred_values[i] >= ref_values[i] + 110) or ((ref_values[i] >= 130 and ref_values[i] <= 180) and (pred_values[i] <= (7/5)*ref_values[i] - 182)):
zone[2] += 1 #Zone C
elif (ref_values[i] >= 240 and (pred_values[i] >= 70 and pred_values[i] <= 180)) or (ref_values[i] <= 175/3 and pred_values[i] <= 180 and pred_values[i] >= 70) or ((ref_values[i] >= 175/3 and ref_values[i] <= 70) and pred_values[i] >= (6/5)*ref_values[i]):
zone[3] += 1 #Zone D
else:
zone[1] += 1 #Zone B
return plt, zone