Detection of cognitive impairment using a machine-learning algorithm

Figures & data

Table 1 Demographics of the dataset

	Sex (M:F)	Age (years)a	ED	MMSEa	KDSQa	DM	HT	DL	Stroke
CI (n=8,627)	2,697:5,930	71.9±8.8	7.2±5.3	21.9±5.9	10.2±7.2	1,852	4,382	1,478	666
NL (n=1,558)	484:1,074	67.5±7.6	7.8±5.2	23.3±5.5	9.2±7.0	309	758	275	106
Total (N=10,185)	3,181:7,004	71.2±8.8	7.3±5.3	22.1±5.8	10.1±7.2	2,161	5,140	1,753	772

Notes:

a P<0.01, statistical significance of the difference between CI and NL as determined by the Student’s t-test. No statistical significance of frequency difference was found in sex, DM, HT, DL, and Stroke between patients with CI and NL with chi-squared test.

Abbreviations: CI, cognitive impairment; DL, dyslipidemia; DM, diabetes mellitus; ED, educational duration; F, female; HT, hypertension; KDSQ, Korean Dementia Screening Questionnaire; M, male; MMSE, Mini-Mental State Examination; NL, normal cognition; Stroke, stroke history.

Figure 1 The ROC curve of three predictors of the KDSQ, MMSE, and the combination of KDSQ and MMSE.

Note: The ROC curves were obtained by conducting a logistic regression using TensorFlow on a dataset from the CREDOS.
Abbreviations: CREDOS, Clinical Research Center for Dementia of South Korea; KDSQ, Korean Dementia Screening Questionnaire; MMSE, Mini-Mental State Examination; ROC, receiver operating characteristic.

Table S1 KDSQ cognition

1. She/He does not know what month and day of the week it is today.

2. She/He cannot find her/his belongings that she/he left somewhere.

3. She/He repeats the same question.

4. She/He forgets to keep her/his word.

5. She/He goes to search for something somewhere, but returns without remembering her/his original purpose.

6. Due to difficulty in remembering something or someone, she/he hesitates to name it or the person.

7. Unable to understand dialog, she/he repeats the same question.

8. She/He has lost her/his way or wandered from place to place.

9. Her/His ability to calculate is worse than before (eg, It is impossible for the patient to calculate commodity prices or changes).

10. Her/His character has changed, compared to the past.

11. She/He is not as good at operating devices (washing machine, electric rice cooker, cultivator, etc.) as before.

12. She/He is not as good at arranging furnishings at home as before.

13. She/He cannot select appropriate clothing for the circumstances.

14. She/He has difficulty with traveling to destinations using mass transportation (difficulties caused by physical disturbances such as arthritis are excluded).

15. She/He fails to change dirty underwear or clothes.

Total score:/:/30

No=0; Sometimes (or a little)=1; Often (or much)=2

Abbreviation: KDSQ, Korean Dementia Screening Questionnaire.

Table S2 The TensorFlow code using CREDOS data to predict CI

import numpy as np

import tensorflow as tf

# each set was developed to create feature (x_data) and outcome (y_data) variables

xy = np.loadtxt(‘CRCD_KDSQ_MMSE_train.txt’, unpack=True, dtype=‘float32’)

x_data = xy[0:−1]

y_data = xy[−1]

# model training with the training dataset

X = tf.placeholder(tf.float32)

Y = tf.placeholder(tf.float32)

W = tf.Variable(tf.random_uniform([1, len(x_data)], −1.0, 1.0))

h = tf.matmul(W, X)

hypothesis = tf.div(1., 1. + tf.exp(−h))

cost = −tf.reduce_mean(Y * tf.log(hypothesis) + (1 − Y) * tf.log(1 − hypothesis))

a = tf.Variable(0.15)

optimizer = tf.train.GradientDescentOptimizer(a)

train = optimizer.minimize(cost)

# calculation of the accuracy with the test dataset

xy = np.loadtxt(‘CRCD_KDSQ_MMSE_test.txt’, unpack=True, dtype=‘float32’)

x_data = xy[0:−1]

y_data = xy[−1]

print (sess.run(hypothesis, feed_dict={X: x_data, Y: y_data}))

answer = tf.equal(tf.floor(hypothesis +0.4), Y)

accuracy = tf.reduce_mean(tf.cast(answer, “float”))

print (“Accuracy: ”, accuracy.eval(session=sess, feed_dict={X: x_data, Y: y_data}))

sess.close()

Abbreviations: CI, cognitive impairment; CREDOS, Clinical Research Center for Dementia of South Korea.