Overview

Dataset statistics

Number of variables9
Number of observations38
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory83.5 B

Variable types

Categorical2
Text1
Numeric6

Dataset

Description장애연금 재심사 장애유형별 심사결과(연간) 현황 입니다.(1급,2급,3급,4급,등급외, 미해당(결정보류,자격미달,확인불가))
URLhttps://www.data.go.kr/data/15094327/fileData.do

Alerts

미해당(결정보류-자격미달-확인불가) has constant value ""Constant
총계 is highly overall correlated with 1급 and 4 other fieldsHigh correlation
1급 is highly overall correlated with 총계 and 2 other fieldsHigh correlation
2급 is highly overall correlated with 총계 and 4 other fieldsHigh correlation
3급 is highly overall correlated with 총계 and 4 other fieldsHigh correlation
4급 is highly overall correlated with 총계 and 2 other fieldsHigh correlation
등급외 is highly overall correlated with 총계 and 2 other fieldsHigh correlation
총계 has unique valuesUnique
1급 has 9 (23.7%) zerosZeros
2급 has 4 (10.5%) zerosZeros
3급 has 1 (2.6%) zerosZeros
등급외 has 9 (23.7%) zerosZeros

Reproduction

Analysis started2023-12-12 11:20:18.939465
Analysis finished2023-12-12 11:20:26.995979
Duration8.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연도
Categorical

Distinct2
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size436.0 B
2021
19 
2022
19 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 19
50.0%
2022 19
50.0%

Length

2023-12-12T20:20:27.130316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:20:27.318808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 19
50.0%
2022 19
50.0%

장애유형
Text

Distinct19
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-12T20:20:27.575433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length12
Mean length9.1578947
Min length3

Characters and Unicode

Total characters348
Distinct characters56
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row눈의 장애
2nd row귀의 장애
3rd row입의 장애
4th row지체의 장애(팔)
5th row지체의 장애(다리)
ValueCountFrequency (%)
장애 22
26.2%
지체의 12
 
14.3%
사지마비 4
 
4.8%
등 4
 
4.8%
눈의 2
 
2.4%
신장의 2
 
2.4%
장애(척수손상 2
 
2.4%
장애(뇌손상 2
 
2.4%
악성신생물(고형암)의 2
 
2.4%
안면의 2
 
2.4%
Other values (15) 30
35.7%
2023-12-12T20:20:28.111085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
46
 
13.2%
장 42
 
12.1%
애 36
 
10.3%
의 36
 
10.3%
지 18
 
5.2%
( 14
 
4.0%
) 14
 
4.0%
체 12
 
3.4%
신 8
 
2.3%
기 6
 
1.7%
Other values (46) 116
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 270
77.6%
Space Separator 46
 
13.2%
Open Punctuation 14
 
4.0%
Close Punctuation 14
 
4.0%
Other Punctuation 4
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 42
15.6%
애 36
 
13.3%
의 36
 
13.3%
지 18
 
6.7%
체 12
 
4.4%
신 8
 
3.0%
기 6
 
2.2%
사 6
 
2.2%
비 6
 
2.2%
마 6
 
2.2%
Other values (42) 94
34.8%
Space Separator
ValueCountFrequency (%)
46
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Other Punctuation
ValueCountFrequency (%)
· 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 270
77.6%
Common 78
 
22.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 42
15.6%
애 36
 
13.3%
의 36
 
13.3%
지 18
 
6.7%
체 12
 
4.4%
신 8
 
3.0%
기 6
 
2.2%
사 6
 
2.2%
비 6
 
2.2%
마 6
 
2.2%
Other values (42) 94
34.8%
Common
ValueCountFrequency (%)
46
59.0%
( 14
 
17.9%
) 14
 
17.9%
· 4
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 270
77.6%
ASCII 74
 
21.3%
None 4
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
46
62.2%
( 14
 
18.9%
) 14
 
18.9%
Hangul
ValueCountFrequency (%)
장 42
15.6%
애 36
 
13.3%
의 36
 
13.3%
지 18
 
6.7%
체 12
 
4.4%
신 8
 
3.0%
기 6
 
2.2%
사 6
 
2.2%
비 6
 
2.2%
마 6
 
2.2%
Other values (42) 94
34.8%
None
ValueCountFrequency (%)
· 4
100.0%

총계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean796.78947
Minimum7
Maximum5439
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T20:20:28.386410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile11.55
Q162.75
median125.5
Q3817.25
95-th percentile4875.5
Maximum5439
Range5432
Interquartile range (IQR)754.5

Descriptive statistics

Standard deviation1522.3435
Coefficient of variation (CV)1.9105969
Kurtosis4.7023929
Mean796.78947
Median Absolute Deviation (MAD)100.5
Skewness2.4223869
Sum30278
Variance2317529.8
MonotonicityNot monotonic
2023-12-12T20:20:28.618154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
430 1
 
2.6%
4400 1
 
2.6%
209 1
 
2.6%
99 1
 
2.6%
120 1
 
2.6%
46 1
 
2.6%
928 1
 
2.6%
92 1
 
2.6%
65 1
 
2.6%
130 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
7 1
2.6%
9 1
2.6%
12 1
2.6%
13 1
2.6%
19 1
2.6%
31 1
2.6%
33 1
2.6%
46 1
2.6%
52 1
2.6%
62 1
2.6%
ValueCountFrequency (%)
5439 1
2.6%
5411 1
2.6%
4781 1
2.6%
4400 1
2.6%
1442 1
2.6%
1410 1
2.6%
1039 1
2.6%
1004 1
2.6%
991 1
2.6%
928 1
2.6%

1급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.078947
Minimum0
Maximum777
Zeros9
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T20:20:28.845806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8.5
Q357
95-th percentile383.35
Maximum777
Range777
Interquartile range (IQR)56

Descriptive statistics

Standard deviation154.43084
Coefficient of variation (CV)2.2036696
Kurtosis12.123596
Mean70.078947
Median Absolute Deviation (MAD)8.5
Skewness3.3053216
Sum2663
Variance23848.885
MonotonicityNot monotonic
2023-12-12T20:20:29.089956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 9
23.7%
1 4
 
10.5%
16 2
 
5.3%
2 2
 
5.3%
3 2
 
5.3%
17 2
 
5.3%
67 1
 
2.6%
5 1
 
2.6%
106 1
 
2.6%
408 1
 
2.6%
Other values (13) 13
34.2%
ValueCountFrequency (%)
0 9
23.7%
1 4
10.5%
2 2
 
5.3%
3 2
 
5.3%
5 1
 
2.6%
8 1
 
2.6%
9 1
 
2.6%
12 1
 
2.6%
16 2
 
5.3%
17 2
 
5.3%
ValueCountFrequency (%)
777 1
2.6%
408 1
2.6%
379 1
2.6%
301 1
2.6%
130 1
2.6%
119 1
2.6%
106 1
2.6%
93 1
2.6%
67 1
2.6%
62 1
2.6%

2급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean414.31579
Minimum0
Maximum4250
Zeros4
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T20:20:29.329760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median28.5
Q356.25
95-th percentile3257.8
Maximum4250
Range4250
Interquartile range (IQR)47.25

Descriptive statistics

Standard deviation1113.742
Coefficient of variation (CV)2.6881476
Kurtosis6.407841
Mean414.31579
Median Absolute Deviation (MAD)21
Skewness2.7867493
Sum15744
Variance1240421.2
MonotonicityNot monotonic
2023-12-12T20:20:29.589861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 4
 
10.5%
33 2
 
5.3%
9 2
 
5.3%
3 2
 
5.3%
49 2
 
5.3%
54 1
 
2.6%
7 1
 
2.6%
87 1
 
2.6%
3931 1
 
2.6%
16 1
 
2.6%
Other values (21) 21
55.3%
ValueCountFrequency (%)
0 4
10.5%
3 2
5.3%
5 1
 
2.6%
7 1
 
2.6%
8 1
 
2.6%
9 2
5.3%
10 1
 
2.6%
13 1
 
2.6%
14 1
 
2.6%
15 1
 
2.6%
ValueCountFrequency (%)
4250 1
2.6%
3931 1
2.6%
3139 1
2.6%
2970 1
2.6%
330 1
2.6%
277 1
2.6%
103 1
2.6%
87 1
2.6%
67 1
2.6%
57 1
2.6%

3급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.78947
Minimum0
Maximum1550
Zeros1
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T20:20:29.822650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.55
Q118.5
median38
Q3100.75
95-th percentile792.65
Maximum1550
Range1550
Interquartile range (IQR)82.25

Descriptive statistics

Standard deviation362.75635
Coefficient of variation (CV)1.815693
Kurtosis5.8968884
Mean199.78947
Median Absolute Deviation (MAD)26.5
Skewness2.4392403
Sum7592
Variance131592.17
MonotonicityNot monotonic
2023-12-12T20:20:30.073278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
15 2
 
5.3%
38 2
 
5.3%
82 1
 
2.6%
20 1
 
2.6%
45 1
 
2.6%
18 1
 
2.6%
670 1
 
2.6%
23 1
 
2.6%
36 1
 
2.6%
35 1
 
2.6%
Other values (26) 26
68.4%
ValueCountFrequency (%)
0 1
2.6%
1 1
2.6%
4 1
2.6%
7 1
2.6%
9 1
2.6%
10 1
2.6%
13 1
2.6%
15 2
5.3%
18 1
2.6%
20 1
2.6%
ValueCountFrequency (%)
1550 1
2.6%
1295 1
2.6%
704 1
2.6%
670 1
2.6%
663 1
2.6%
599 1
2.6%
504 1
2.6%
490 1
2.6%
118 1
2.6%
107 1
2.6%

4급
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.105263
Minimum2
Maximum479
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T20:20:30.820157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.85
Q110.5
median40
Q397
95-th percentile267.65
Maximum479
Range477
Interquartile range (IQR)86.5

Descriptive statistics

Standard deviation113.06487
Coefficient of variation (CV)1.3443257
Kurtosis4.7074839
Mean84.105263
Median Absolute Deviation (MAD)33.5
Skewness2.1468354
Sum3196
Variance12783.664
MonotonicityNot monotonic
2023-12-12T20:20:31.084462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
4 3
 
7.9%
2 2
 
5.3%
41 2
 
5.3%
40 2
 
5.3%
74 2
 
5.3%
9 2
 
5.3%
106 1
 
2.6%
65 1
 
2.6%
16 1
 
2.6%
225 1
 
2.6%
Other values (21) 21
55.3%
ValueCountFrequency (%)
2 2
5.3%
3 1
 
2.6%
4 3
7.9%
8 1
 
2.6%
9 2
5.3%
10 1
 
2.6%
12 1
 
2.6%
14 1
 
2.6%
16 1
 
2.6%
17 1
 
2.6%
ValueCountFrequency (%)
479 1
2.6%
430 1
2.6%
239 1
2.6%
226 1
2.6%
225 1
2.6%
217 1
2.6%
182 1
2.6%
151 1
2.6%
106 1
2.6%
102 1
2.6%

등급외
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.5
Minimum0
Maximum389
Zeros9
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T20:20:31.333682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile169.85
Maximum389
Range389
Interquartile range (IQR)3

Descriptive statistics

Standard deviation87.072741
Coefficient of variation (CV)3.0551839
Kurtosis12.56578
Mean28.5
Median Absolute Deviation (MAD)2
Skewness3.603115
Sum1083
Variance7581.6622
MonotonicityNot monotonic
2023-12-12T20:20:31.573411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 9
23.7%
2 6
15.8%
3 5
13.2%
1 5
13.2%
4 5
13.2%
11 1
 
2.6%
119 1
 
2.6%
6 1
 
2.6%
356 1
 
2.6%
5 1
 
2.6%
Other values (3) 3
 
7.9%
ValueCountFrequency (%)
0 9
23.7%
1 5
13.2%
2 6
15.8%
3 5
13.2%
4 5
13.2%
5 1
 
2.6%
6 1
 
2.6%
8 1
 
2.6%
11 1
 
2.6%
119 1
 
2.6%
ValueCountFrequency (%)
389 1
 
2.6%
356 1
 
2.6%
137 1
 
2.6%
119 1
 
2.6%
11 1
 
2.6%
8 1
 
2.6%
6 1
 
2.6%
5 1
 
2.6%
4 5
13.2%
3 5
13.2%

미해당(결정보류-자격미달-확인불가)
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
0
38 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 38
100.0%

Length

2023-12-12T20:20:31.810614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:20:31.979491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 38
100.0%

Interactions

2023-12-12T20:20:25.427385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:19.356909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:20.859027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:22.006005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:23.068896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:24.329067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:25.611215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:19.501053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:21.044510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:22.168209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:23.266818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:24.534662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:25.809348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:20.173230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:21.244054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:22.348761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:23.484555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:24.761733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:25.982645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:20.308389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:21.401091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:22.497476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:23.626970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:24.906744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:26.171737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:20.482046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:21.594059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:22.693977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:23.812222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:25.091203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:26.350464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:20.646527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:21.786454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:22.863678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:24.084805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:25.243285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:20:32.096137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준연도장애유형총계1급2급3급4급등급외
기준연도1.0000.0000.0000.0000.0000.0000.0000.000
장애유형0.0001.0001.0000.6420.8170.8350.9531.000
총계0.0001.0001.0000.7770.8240.9720.8400.814
1급0.0000.6420.7771.0000.9040.8680.0000.918
2급0.0000.8170.8240.9041.0000.8260.6130.652
3급0.0000.8350.9720.8680.8261.0000.6150.770
4급0.0000.9530.8400.0000.6130.6151.0000.730
등급외0.0001.0000.8140.9180.6520.7700.7301.000
2023-12-12T20:20:32.275551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총계1급2급3급4급등급외기준연도
총계1.0000.6670.9300.8620.8030.6390.000
1급0.6671.0000.7310.6670.3950.4020.000
2급0.9300.7311.0000.7670.6730.5690.000
3급0.8620.6670.7671.0000.5820.6400.000
4급0.8030.3950.6730.5821.0000.3900.000
등급외0.6390.4020.5690.6400.3901.0000.000
기준연도0.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T20:20:26.603341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:20:26.885984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

기준연도장애유형총계1급2급3급4급등급외미해당(결정보류-자격미달-확인불가)
02021눈의 장애43067548222520
12021귀의 장애6200214100
22021입의 장애19159400
32021지체의 장애(팔)1981171077030
42021지체의 장애(다리)109015108220
52021지체의 장애(척추)1051624253910
62021지체의 장애(사지마비)3323151210
72021정신 또는 신경계통의 장애10049310370410220
82021호흡기의 장애1021634381040
92021심장의 장애520338830
기준연도장애유형총계1급2급3급4급등급외미해당(결정보류-자격미달-확인불가)
282022심장의 장애6519361450
292022신장의 장애4400239313543020
302022간의 장애1301216207480
312022혈액·조혈기의장애103931576631511370
322022복부·골반장기의 장애14293981940
332022안면의 장애9007200
342022악성신생물(고형암)의 장애541130131391550323890
352022지체의 장애(뇌손상 등 사지마비)141040827750421740
362022지체의 장애(척수손상 등 사지마비)23210636602910
372022미분류12080400