International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 4 Issue 6, September-October 2020 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
Diabetes Prediction Model
Mitadru Banerjee Chowdhury
Systems Engineer, TCS, Kolkata, West Bengal, India
ABSTRACT
My mother has been a diabetic for the last 15 years of her life. I have known
the difference between the Fasting and BP sugar levels for a long time.
Therefore, when I found a public data set of the diabetes levels against age,
blood pressure, and BMI, it got me thinking if I could map the relationship
between the multiple factors and figure out how these factors can have an
impact on the blood sugar levels.
How to cite this paper: Mitadru Banerjee
Chowdhury "Diabetes Prediction Model"
Published
in
International Journal
of Trend in Scientific
Research
and
Development
(ijtsrd), ISSN: 24566470, Volume-4 |
IJTSRD33276
Issue-6,
October
2020,
pp.121-141,
URL:
www.ijtsrd.com/papers/ijtsrd33276.pdf
In this paper, I attempt to map the relation between the multiple factors,
affecting the blood sugar levels. I will be using the R Studio, using the R
Programming language to implement the project.
KEYWORDS: Forecast, ggplot2, Predict, Scales
Copyright © 2020 by author(s) and
International Journal of Trend in Scientific
Research and Development Journal. This
is an Open Access article distributed
under the terms of
the
Creative
Commons Attribution
License
(CC
BY
4.0)
(http://creativecommons.org/licenses/by
/4.0)
Data Set Used
Pregnancies
Glucose
6
1
8
1
0
5
3
10
2
8
4
10
10
1
5
7
0
7
1
1
3
8
7
9
11
10
7
1
13
148
85
183
89
137
116
78
115
197
125
110
168
139
189
166
100
118
107
103
115
126
99
196
119
143
125
147
97
145
@ IJTSRD
|
Blood
Pressure
72
66
64
66
40
74
50
0
70
96
92
74
80
60
72
0
84
74
30
70
88
84
90
80
94
70
76
66
82
Skin
Thickness
35
29
0
23
35
0
32
0
45
0
0
0
0
23
19
0
47
0
38
30
41
0
0
35
33
26
0
15
19
Unique Paper ID – IJTSRD33276
|
Insulin
BMI
0
0
0
94
168
0
88
0
543
0
0
0
0
846
175
0
230
0
83
96
235
0
0
0
146
115
0
140
110
33.6
26.6
23.3
28.1
43.1
25.6
31
35.3
30.5
0
37.6
38
27.1
30.1
25.8
30
45.8
29.6
43.3
34.6
39.3
35.4
39.8
29
36.6
31.1
39.4
23.2
22.2
Volume – 4 | Issue – 6
Diabetes Pedigree
Function
0.627
0.351
0.672
0.167
2.288
0.201
0.248
0.134
0.158
0.232
0.191
0.537
1.441
0.398
0.587
0.484
0.551
0.254
0.183
0.529
0.704
0.388
0.451
0.263
0.254
0.205
0.257
0.487
0.245
|
Age
Outcome
50
31
32
21
33
30
26
29
53
54
30
34
57
59
51
32
31
31
33
32
27
50
41
29
51
41
43
22
57
1
0
1
0
1
0
1
0
1
1
0
1
0
1
1
1
1
1
0
1
0
0
1
1
1
1
1
0
0
September-October 2020
Page 121
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5
5
3
3
6
10
4
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9
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2
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117
109
158
88
92
122
103
138
102
90
111
180
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180
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105
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176
150
73
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133
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141
114
99
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109
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100
139
126
129
79
0
62
95
131
112
113
74
83
101
137
110
106
100
136
107
80
|
92
75
76
58
92
78
60
76
76
68
72
64
84
92
110
64
66
56
70
66
0
80
50
66
90
66
50
68
88
82
64
0
72
62
58
66
74
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92
66
85
66
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90
86
75
48
78
72
0
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44
0
78
65
108
74
72
68
70
68
55
Unique Paper ID – IJTSRD33276
0
26
36
11
0
31
33
0
37
42
47
25
0
18
24
0
39
0
27
32
0
11
15
21
34
42
10
39
60
0
41
0
0
0
34
0
27
30
0
13
27
20
35
0
20
30
20
0
33
0
22
13
0
26
28
0
29
54
25
32
19
0
|
0
0
245
54
0
0
192
0
0
0
207
70
0
0
240
0
0
0
0
0
0
82
36
23
300
342
0
304
110
0
142
0
0
0
128
0
0
0
0
38
100
90
140
0
270
0
0
0
0
0
0
0
0
71
0
0
125
0
71
110
0
0
34.1
36
31.6
24.8
19.9
27.6
24
33.2
32.9
38.2
37.1
34
40.2
22.7
45.4
27.4
42
29.7
28
39.1
0
19.4
24.2
24.4
33.7
34.7
23
37.7
46.8
40.5
41.5
0
32.9
25
25.4
32.8
29
32.5
42.7
19.6
28.9
32.9
28.6
43.4
35.1
32
24.7
32.6
37.7
43.2
25
22.4
0
29.3
24.6
48.8
32.4
36.6
38.5
37.1
26.5
19.1
Volume – 4 | Issue – 6
0.337
0.546
0.851
0.267
0.188
0.512
0.966
0.42
0.665
0.503
1.39
0.271
0.696
0.235
0.721
0.294
1.893
0.564
0.586
0.344
0.305
0.491
0.526
0.342
0.467
0.718
0.248
0.254
0.962
1.781
0.173
0.304
0.27
0.587
0.699
0.258
0.203
0.855
0.845
0.334
0.189
0.867
0.411
0.583
0.231
0.396
0.14
0.391
0.37
0.27
0.307
0.14
0.102
0.767
0.237
0.227
0.698
0.178
0.324
0.153
0.165
0.258
|
38
60
28
22
28
45
33
35
46
27
56
26
37
48
54
40
25
29
22
31
24
22
26
30
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42
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41
31
44
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21
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38
54
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26
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23
22
22
41
27
26
24
22
22
36
22
37
27
45
26
43
24
21
September-October 2020
0
0
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0
0
0
0
0
1
1
1
0
0
0
1
0
1
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0
1
0
0
0
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1
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1
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
1
0
0
Page 122
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7
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1
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@ IJTSRD
123
81
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142
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92
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84
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100
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129
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106
108
108
154
102
57
106
147
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136
114
156
|
80
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72
82
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62
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50
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72
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65
56
122
58
58
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64
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30
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58
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64
74
68
60
70
60
80
72
78
82
52
66
62
75
80
64
78
70
74
65
86
Unique Paper ID – IJTSRD33276
15
40
0
18
27
28
18
30
51
0
0
0
18
0
29
0
28
31
25
33
26
34
0
32
0
0
0
23
15
56
39
30
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0
42
30
36
24
0
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37
31
13
20
26
25
0
29
0
30
26
0
31
23
37
35
0
17
50
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28
|
176
48
0
64
228
0
76
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220
0
0
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152
0
140
18
36
135
495
37
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175
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0
49
140
50
92
0
325
0
0
63
0
284
0
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119
0
0
204
0
155
32
46.7
23.8
24.7
33.9
31.6
20.4
28.7
49.7
39
26.1
22.5
26.6
39.6
28.7
22.4
29.5
34.3
37.4
33.3
34
31.2
34
30.5
31.2
34
33.7
28.2
23.2
53.2
34.2
33.6
26.8
33.3
55
42.9
33.3
34.5
27.9
29.7
33.3
34.5
38.3
21.1
33.8
30.8
28.7
31.2
36.9
21.1
39.5
32.5
32.4
32.8
0
32.8
30.5
33.7
27.3
37.4
21.9
34.3
Volume – 4 | Issue – 6
0.443
0.261
0.277
0.761
0.255
0.13
0.323
0.356
0.325
1.222
0.179
0.262
0.283
0.93
0.801
0.207
0.287
0.336
0.247
0.199
0.543
0.192
0.391
0.588
0.539
0.22
0.654
0.443
0.223
0.759
0.26
0.404
0.186
0.278
0.496
0.452
0.261
0.403
0.741
0.361
1.114
0.356
0.457
0.647
0.088
0.597
0.532
0.703
0.159
0.268
0.286
0.318
0.272
0.237
0.572
0.096
1.4
0.218
0.085
0.399
0.432
1.189
|
34
42
60
21
40
24
22
23
31
33
22
21
24
27
21
27
37
25
24
24
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38
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September-October 2020
0
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Page 123
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8
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2
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37.7
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42.6
34.2
Volume – 4 | Issue – 6
0.687
0.137
0.337
0.637
0.833
0.229
0.817
0.294
0.204
0.167
0.368
0.743
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0.495
0.18
0.542
0.773
0.678
0.37
0.719
0.382
0.319
0.19
0.956
0.084
0.725
0.299
0.268
0.244
0.745
0.615
1.321
0.64
0.361
0.142
0.374
0.383
0.578
0.136
0.395
0.187
0.678
0.905
0.15
0.874
0.236
0.787
0.235
0.324
0.407
0.605
0.151
0.289
0.355
0.29
0.375
0.164
0.431
0.26
|
23
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September-October 2020
0
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Page 124
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0
37
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271
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0
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0
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0
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0
375
150
130
0
0
0
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67
0
0
0
0
0
56
0
45
0
57
0
41.8
35.8
30
29
37.8
34.6
31.6
25.2
28.8
23.6
34.6
35.7
37.2
36.7
45.2
44
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25.4
35
29.7
43.6
35.9
44.1
30.8
18.4
29.2
33.1
25.6
27.1
38.2
30
31.2
52.3
35.4
30.1
31.2
28
24.4
35.8
27.6
33.6
30.1
28.7
25.9
33.3
30.9
30
32.1
32.4
32
33.6
36.3
40
25.1
27.5
45.6
25.2
23
33.2
34.2
40.5
26.5
Volume – 4 | Issue – 6
0.742
0.514
0.464
1.224
0.261
1.072
0.805
0.209
0.687
0.666
0.101
0.198
0.652
2.329
0.089
0.645
0.238
0.583
0.394
0.293
0.479
0.586
0.686
0.831
0.582
0.192
0.446
0.402
1.318
0.329
1.213
0.258
0.427
0.282
0.143
0.38
0.284
0.249
0.238
0.926
0.543
0.557
0.092
0.655
1.353
0.299
0.761
0.612
0.2
0.226
0.997
0.933
1.101
0.078
0.24
1.136
0.128
0.254
0.422
0.251
0.677
0.296
|
38
25
32
32
41
21
66
37
61
26
22
26
24
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35
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25
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21
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40
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September-October 2020
1
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Page 125
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
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Unique Paper ID – IJTSRD33276
23
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171
27.8
24.9
25.3
37.9
35.9
32.4
30.4
27
26
38.7
45.6
20.8
36.1
36.9
36.6
43.3
40.5
21.9
35.5
28
30.7
36.6
23.6
32.3
31.6
35.8
52.9
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39.7
25.5
24.8
30.5
32.9
26.2
39.4
26.6
29.5
35.9
34.1
19.3
30.5
38.1
23.5
27.5
31.6
27.4
26.8
35.7
25.6
35.1
35.1
45.5
30.8
23.1
32.7
43.3
23.6
23.9
47.9
33.8
31.2
34.2
Volume – 4 | Issue – 6
0.454
0.744
0.881
0.334
0.28
0.262
0.165
0.259
0.647
0.619
0.808
0.34
0.263
0.434
0.757
1.224
0.613
0.254
0.692
0.337
0.52
0.412
0.84
0.839
0.422
0.156
0.209
0.207
0.215
0.326
0.143
1.391
0.875
0.313
0.605
0.433
0.626
1.127
0.315
0.284
0.345
0.15
0.129
0.527
0.197
0.254
0.731
0.148
0.123
0.692
0.2
0.127
0.122
1.476
0.166
0.282
0.137
0.26
0.259
0.932
0.343
0.893
|
23
57
22
28
39
37
47
52
51
34
29
26
33
21
25
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24
65
28
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22
26
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September-October 2020
0
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Page 126
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
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1
1
1
5
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Unique Paper ID – IJTSRD33276
0
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27.2
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32.8
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43.3
32
31.6
32
45.7
23.7
22.1
32.9
27.7
24.7
34.3
21.1
34.9
32
Volume – 4 | Issue – 6
0.331
0.472
0.673
0.389
0.29
0.485
0.349
0.654
0.187
0.279
0.346
0.237
0.252
0.243
0.58
0.559
0.302
0.962
0.569
0.378
0.875
0.583
0.207
0.305
0.52
0.385
0.499
0.368
0.252
0.306
0.234
2.137
1.731
0.545
0.225
0.816
0.528
0.299
0.509
0.238
1.021
0.821
0.236
0.947
1.268
0.221
0.205
0.66
0.239
0.452
0.949
0.444
0.34
0.389
0.463
0.803
1.6
0.944
0.196
0.389
0.241
0.161
|
41
22
36
22
33
57
49
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23
26
37
29
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September-October 2020
1
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Page 127
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
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9
5
2
4
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Unique Paper ID – IJTSRD33276
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36.5
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28.9
39.9
19.6
37.8
33.6
26.7
30.2
37.6
25.9
20.8
21.8
35.3
Volume – 4 | Issue – 6
0.151
0.286
0.28
0.135
0.52
0.376
0.336
1.191
0.702
0.674
0.528
1.076
0.256
0.534
0.258
1.095
0.554
0.624
0.219
0.507
0.561
0.496
0.421
0.516
0.264
0.256
0.328
0.284
0.233
0.108
0.551
0.527
0.167
1.138
0.205
0.244
0.434
0.147
0.727
0.435
0.497
0.23
0.955
0.38
2.42
0.658
0.33
0.51
0.285
0.415
0.542
0.381
0.832
0.498
0.212
0.687
0.364
1.001
0.46
0.733
0.416
0.705
|
55
35
38
41
26
46
25
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28
28
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21
21
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September-October 2020
0
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Page 128
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
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10
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8
6
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Unique Paper ID – IJTSRD33276
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27.6
24
21.8
27.8
36.8
30
46.1
41.3
33.2
38.8
29.9
28.9
27.3
33.7
23.8
25.9
28
35.5
35.2
27.8
38.2
44.2
42.3
40.7
46.5
25.6
26.1
36.8
33.5
32.8
28.9
0
26.6
26
30.1
25.1
29.3
25.2
37.2
39
33.3
37.3
33.3
36.5
28.6
30.4
25
29.7
22.1
24.2
27.3
25.6
31.6
30.3
37.6
32.8
19.6
25
33.2
0
34.2
31.6
Volume – 4 | Issue – 6
0.258
1.022
0.452
0.269
0.6
0.183
0.571
0.607
0.17
0.259
0.21
0.126
0.231
0.711
0.466
0.162
0.419
0.344
0.197
0.306
0.233
0.63
0.365
0.536
1.159
0.294
0.551
0.629
0.292
0.145
1.144
0.174
0.304
0.292
0.547
0.163
0.839
0.313
0.267
0.727
0.738
0.238
0.263
0.314
0.692
0.968
0.409
0.297
0.207
0.2
0.525
0.154
0.268
0.771
0.304
0.18
0.582
0.187
0.305
0.189
0.652
0.151
|
37
34
21
22
25
38
27
28
22
22
50
24
59
29
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21
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58
22
24
28
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25
26
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September-October 2020
0
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Page 129
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
3
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Unique Paper ID – IJTSRD33276
18
19
15
31
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18
0
52
0
30
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25
32
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18.2
26.3
30.8
24.6
29.8
45.3
41.3
29.8
33.3
32.9
29.6
21.7
36.3
36.4
39.4
32.4
34.9
39.5
32
34.5
43.6
33.1
32.8
28.5
27.4
31.9
27.8
29.9
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41.3
37.6
26.9
32.4
26.1
38.6
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31.3
34.3
32.5
22.6
29.5
34.7
30.1
35.5
24
42.9
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34.7
42.1
25
26.5
38.7
28.7
22.5
34.9
Volume – 4 | Issue – 6
0.444
0.299
0.107
0.493
0.66
0.717
0.686
0.917
0.501
1.251
0.302
0.197
0.735
0.804
0.968
0.661
0.549
0.825
0.159
0.365
0.423
1.034
0.16
0.341
0.68
0.204
0.591
0.247
0.422
0.471
0.161
0.218
0.237
0.126
0.3
0.121
0.502
0.401
0.497
0.601
0.748
0.412
0.085
0.338
0.203
0.27
0.268
0.43
0.198
0.892
0.28
0.813
0.693
0.245
0.575
0.371
0.206
0.259
0.19
0.687
0.417
0.129
|
21
21
24
22
31
22
24
29
31
24
23
46
67
23
32
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23
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21
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September-October 2020
0
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Page 130
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
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Unique Paper ID – IJTSRD33276
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24.3
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21.1
46.8
39.4
34.4
28.5
33.6
32
45.3
27.8
36.8
23.1
27.1
23.7
27.8
35.2
28.4
35.8
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19.5
41.5
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30.9
32.9
38.2
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28.7
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28.2
32.4
38.4
24.2
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43.5
30.8
37.7
24.7
32.4
34.6
24.7
27.4
34.5
26.2
27.5
25.9
31.2
28.8
31.6
40.9
19.5
29.3
34.3
29.5
28
27.6
39.4
23.4
37.8
28.3
Volume – 4 | Issue – 6
0.249
1.154
0.342
0.925
0.175
0.402
1.699
0.733
0.682
0.194
0.559
0.088
0.407
0.4
0.19
0.1
0.692
0.212
0.514
1.258
0.482
0.27
0.138
0.292
0.593
0.787
0.878
0.557
0.207
0.157
0.257
1.282
0.141
0.246
1.698
1.461
0.347
0.158
0.362
0.206
0.393
0.144
0.148
0.732
0.238
0.343
0.115
0.167
0.465
0.153
0.649
0.871
0.149
0.695
0.303
0.178
0.61
0.73
0.134
0.447
0.455
0.26
|
29
52
25
45
24
44
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22
46
21
38
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28
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27
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September-October 2020
0
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Page 131
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
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35.9
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38.5
28.4
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34.4
32.8
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31.2
Volume – 4 | Issue – 6
0.133
0.234
0.466
0.269
0.455
0.142
0.24
0.155
1.162
0.19
1.292
0.182
1.394
0.165
0.637
0.245
0.217
0.235
0.141
0.43
0.164
0.631
0.551
0.285
0.88
0.587
0.328
0.23
0.263
0.127
0.614
0.332
0.364
0.366
0.536
0.64
0.591
0.314
0.181
0.828
0.335
0.856
0.257
0.886
0.439
0.191
0.128
0.268
0.253
0.598
0.904
0.483
0.565
0.905
0.304
0.118
0.177
0.261
0.176
0.148
0.674
0.295
|
23
23
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September-October 2020
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Page 132
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0.595
0.415
0.378
0.317
0.289
0.349
0.251
0.265
0.236
0.496
0.433
0.326
0.141
0.323
0.259
0.646
0.426
0.56
0.284
0.515
0.6
0.453
0.293
0.785
0.4
0.219
0.734
1.174
0.488
0.358
1.096
0.408
0.178
1.182
0.261
0.223
0.222
0.443
1.057
0.391
0.258
0.197
0.278
0.766
0.403
0.142
0.171
0.34
0.245
0.349
0.315
40
38
21
32
34
31
56
24
52
34
21
42
42
45
38
25
22
22
22
34
22
24
22
53
28
21
42
21
42
48
26
22
45
39
46
27
32
36
50
22
28
25
26
45
37
39
52
26
66
22
43
33
63
27
30
47
23
0
1
0
0
1
1
0
0
1
0
0
1
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
1
1
0
0
1
0
0
1
0
1
1
1
0
0
1
1
1
0
1
0
1
0
1
0
0
0
0
1
0
Goals:
 Plotting the Glucose levels with the Age, BP, Insulin levels.
 Removing the outliers from the data set.
 Creating a model of the Glucose levels against the Age, BP, Insulin levels.
 Plotting the predicted values of the Glucose levels.
@ IJTSRD
|
Unique Paper ID – IJTSRD33276
|
Volume – 4 | Issue – 6
|
September-October 2020
Page 133
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
Flow of the Project
Start: Installation of the packages, and loading of the data.
Input: Data in the form of the csv file will be loaded and data points will be isolated.
Processing the data: Isolate the data and create the data frame of Diabetes.
Plot the data points: Plot the data points, specifically the glucose levels against the BP, Age and BMI.
Removal of Outliers: Identify the outliers in the data set and remove them from the data set.
Create the model of the data: Create multiple models of the data set.
Compare the models: Compare the different models created, find the AIC values and use the one, which has the least
value.
 Predict the values from the model: Use the ‘Predict’ function to predict the values of the selected model.
 Plot the forecast values: Plot the ‘Predicted’ values.
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Packages Used
 ggplot2: To plot the model.
 Forecast: To forecast the time series of the data set.
 Scales: Used to dress the data set.
Flow with Plots
#Loading the Input File:
DiabetesRawData<-read.csv(file.choose())
Load the raw data from the diabetes data set.
The data set used in this project has been attached.
#Plot of the Raw Data:
#Plot of the Glucose Levels
qplot(Diabetes$Glucose,main ='Plot of the Glucose Levels of the Raw Data',xlab = 'Glucose Levels', colour=I("red"))
@ IJTSRD
|
Unique Paper ID – IJTSRD33276
|
Volume – 4 | Issue – 6
|
September-October 2020
Page 134
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
#Plotting the Glucose Levels
Diabetes<-na.omit(DiabetesRawData)
ggplot(Diabetes,aes(x=Age,y=Glucose))+geom_point()
ggplot(Diabetes,aes(x=Age,y=Glucose))+geom_point()+labs(x='Age',y='Glucose Level',title='Plot of the Age against Glucose
Levels')
#We have to remove the outliers from the Graph; we will eliminate the Records having Glucose levels less than 50.
Diabetes<-Diabetes[Diabetes$Glucose>50,]
ggplot(Diabetes,aes(x=Age,y=Glucose))+geom_point()+labs(x='Age',y='Glucose Level', title='Plot of the Age against Glucose
Levels')
#Plot the Age against the BP, to isolate and eliminate the outlier values of BP
ggplot(Diabetes,aes(x=Age,y=BloodPressure))+geom_point()+labs(x='Age',y='Glucose Level',title='Plot of the Age against Blood
Pressure Levels')
@ IJTSRD
|
Unique Paper ID – IJTSRD33276
|
Volume – 4 | Issue – 6
|
September-October 2020
Page 135
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
Diabetes<-Diabetes[Diabetes$BloodPressure>25,]
ggplot(Diabetes,aes(x=Age,y=BloodPressure))+geom_point()+labs(x='Age',y='Glucose Level',title='Plot of the Age against Blood
Pressure Levels')
#Outliers have been removed for the BP
#Checking for Insulin Levels
ggplot(Diabetes,aes(x=Age,y=Insulin))+geom_point()+labs(x='Age',y='Insulin Level',title='Plot of the Age against Insulin
Levels')
@ IJTSRD
|
Unique Paper ID – IJTSRD33276
|
Volume – 4 | Issue – 6
|
September-October 2020
Page 136
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
#Clearing the Outliers from the data set.
Diabetes<-Diabetes[Diabetes$Insulin<400,]
Diabetes<-Diabetes[Diabetes$Insulin>5,]
ggplot(Diabetes,aes(x=Age,y=Insulin))+geom_point()+labs(x='Age',y='Insulin Level',title='Plot of the Age against Insulin
Levels')
#Checking the health of BMI data
ggplot(DiabetesRawData,aes(x=Age,y=BMI))+geom_point()+labs(x='Age',y='BMI',title='Plot of the Age against BMI')
@ IJTSRD
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Unique Paper ID – IJTSRD33276
|
Volume – 4 | Issue – 6
|
September-October 2020
Page 137
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
Diabetes<-Diabetes[Diabetes$BMI<60,]
Diabetes<-Diabetes[Diabetes$BMI>10,]
ggplot(Diabetes,aes(x=Age,y=Insulin))+geom_point()+labs(x='Age',y='BMI',title='Plot of the Age against BMI')
#Checking the health of the Diabetes Pedigree Function.
ggplot(Diabetes,aes(x=Age,y=DiabetesPedigreeFunction))+geom_point()+labs(x='Age',y='Diabetes
',title='Plot of the Age against Diabetes Pedigree Function')
@ IJTSRD
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Unique Paper ID – IJTSRD33276
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Volume – 4 | Issue – 6
|
Pedigree
September-October 2020
Function
Page 138
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
Diabetes<-Diabetes[Diabetes$DiabetesPedigreeFunction<2.0,]
ggplot(Diabetes,aes(x=Age,y=DiabetesPedigreeFunction))+geom_point()+labs(x='Age',y='Diabetes
',title='Plot of the Age against Diabetes Pedigree Function')
Pedigree
Function
#Now that we have a cleaned up data set, we can now start working on creating models from this data set,we will be training the
model on the 'Diabetes' data set, but we will need a new data set to test it on. So we create a new data set which can be tested upon.
DiabetesTest<-sapply(Diabetes,rep.int, times=3)
DiabetesTest<-dat.frame(DiabetesTest)
#This creates a DiabetesTest data frame, which is repeated 3 times. This data set can be used to test the Model.
DiabetesModel1<-glm(Glucose~BMI*Insulin*Age*BloodPressure,data = Diabetes)
DiabetesModel2<-glm(Glucose~BMI+Insulin+Age+BloodPressure,data = Diabetes)
DiabetesModel3<-glm(Glucose~BMI:Insulin:Age:BloodPressure,data = Diabetes)
@ IJTSRD
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Unique Paper ID – IJTSRD33276
|
Volume – 4 | Issue – 6
|
September-October 2020
Page 139
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
DiabetesModel4<-glm(Glucose~BMI*Insulin*Age:BloodPressure,data = Diabetes)
DiabetesModel5<-glm(Glucose~BMI*Insulin*Age+BloodPressure,data = Diabetes)
DiabetesModel6<-glm(Glucose~BMI*Insulin+Age+BloodPressure,data = Diabetes)
DiabetesModel7<-glm(Glucose~BMI*Insulin:Age+BloodPressure,data = Diabetes)
DiabetesModel8<-glm(Glucose~BMI+Insulin*Age*BloodPressure,data = Diabetes)
DiabetesModel9<-glm(Glucose~BMI:Insulin*Age*BloodPressure,data = Diabetes)
#We will do a multiplot of the models, and then compare them by using the AIC.
multiplot(DiabetesModel1,DiabetesModel2,DiabetesModel3)
AIC(DiabetesModel1,DiabetesModel2,DiabetesModel3,DiabetesModel4,DiabetesModel5,DiabetesModel6,DiabetesModel7,Diabe
tesModel8,DiabetesModel9)
df AIC
DiabetesModel1 17 3374.796
DiabetesModel2 6 3365.353
DiabetesModel3 3 3423.790
DiabetesModel4 9 3363.508
DiabetesModel5 10 3365.492
DiabetesModel6 7 3367.345
DiabetesModel7 6 3399.525
DiabetesModel8 10 3368.397
DiabetesModel9 9 3380.641
#The DiabetesModel4 has the least value of AIC of the corresponding models. Hence this shall be considered to predict the values.
ggplot(DiabetesModel4, aes(x=Age,y=Glucose))+geom_point()+theme_economist()
#Predict the values of the Model, based on a test data set
DiabetesPrediction<-predict(DiabetesModel4,DiabetesTest)
DiabetesPrediction<-predict(DiabetesModel4,DiabetesTest,se.fit = TRUE) #This creates the fit plot of the Prediction.
#Plot of the Predicted Glucose Levels
qplot(Diabetes$Glucose,main ='Plot of the Predicted Values of Blood Glucose Levels against the number of readings',xlab =
'Predicted Glucose Levels', colour=I("red"))
@ IJTSRD
|
Unique Paper ID – IJTSRD33276
|
Volume – 4 | Issue – 6
|
September-October 2020
Page 140
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
Conclusion
 Loaded the data.
 Removed the outliers.
 Plotted the data points.
 We found the relation between the factors influencing
the blood glucose levels.
@ IJTSRD
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Unique Paper ID – IJTSRD33276
|

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Create the models.
Compare the multiple models.
Predict the Glucose levels based on the models.
Plot the range of predicted values.
Volume – 4 | Issue – 6
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September-October 2020
Page 141