R Notebook
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## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union## corrplot 0.92 loadedIntroduction
This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective is to predict based on diagnostic measurements whether a patient has diabetes.
Data Dictionary
Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.
Pregnancies: Number of times pregnant
Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test
BloodPressure: Diastolic blood pressure (mm Hg)
SkinThickness: Triceps skin fold thickness (mm)
Insulin: 2-Hour serum insulin (mu U/ml)
BMI: Body mass index (weight in kg/(height in m)^2)
DiabetesPedigreeFunction: Diabetes pedigree function. Indicates the function which scores likelihood of diabetes based on family history
Age: Age (years)
Outcome: Class variable (0 or 1). If patient had diabetes 1 = Yes, 0 = No.
Understand the data
The data was imported into R and run head to visualize the first 6 rows
## Pregnancies Glucose BloodPressure SkinThickness Insulin BMI
## 1 6 148 72 35 0 33.6
## 2 1 85 66 29 0 26.6
## 3 8 183 64 0 0 23.3
## 4 1 89 66 23 94 28.1
## 5 0 137 40 35 168 43.1
## 6 5 116 74 0 0 25.6
## DiabetesPedigreeFunction Age Outcome
## 1 0.627 50 1
## 2 0.351 31 0
## 3 0.672 32 1
## 4 0.167 21 0
## 5 2.288 33 1
## 6 0.201 30 0## Pregnancies Glucose BloodPressure SkinThickness
## Min. : 0.000 Min. : 0.0 Min. : 0.00 Min. : 0.00
## 1st Qu.: 1.000 1st Qu.: 99.0 1st Qu.: 62.00 1st Qu.: 0.00
## Median : 3.000 Median :117.0 Median : 72.00 Median :23.00
## Mean : 3.845 Mean :120.9 Mean : 69.11 Mean :20.54
## 3rd Qu.: 6.000 3rd Qu.:140.2 3rd Qu.: 80.00 3rd Qu.:32.00
## Max. :17.000 Max. :199.0 Max. :122.00 Max. :99.00
## Insulin BMI DiabetesPedigreeFunction Age
## Min. : 0.0 Min. : 0.00 Min. :0.0780 Min. :21.00
## 1st Qu.: 0.0 1st Qu.:27.30 1st Qu.:0.2437 1st Qu.:24.00
## Median : 30.5 Median :32.00 Median :0.3725 Median :29.00
## Mean : 79.8 Mean :31.99 Mean :0.4719 Mean :33.24
## 3rd Qu.:127.2 3rd Qu.:36.60 3rd Qu.:0.6262 3rd Qu.:41.00
## Max. :846.0 Max. :67.10 Max. :2.4200 Max. :81.00
## Outcome
## Min. :0.000
## 1st Qu.:0.000
## Median :0.000
## Mean :0.349
## 3rd Qu.:1.000
## Max. :1.000Also looked at the dimensions of the data. The dataset had 768 rows and 9 columns
dim(diabetes)## [1] 768 9colnames(diabetes)## [1] "Pregnancies" "Glucose"
## [3] "BloodPressure" "SkinThickness"
## [5] "Insulin" "BMI"
## [7] "DiabetesPedigreeFunction" "Age"
## [9] "Outcome"Looking if we had any null values in the data or any duplicated values. No null values were present.
## $Pregnancies
## [1] 0
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## $Glucose
## [1] 0
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## $BloodPressure
## [1] 0
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## $SkinThickness
## [1] 0
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## $Insulin
## [1] 0
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## $BMI
## [1] 0
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## $DiabetesPedigreeFunction
## [1] 0
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## $Age
## [1] 0
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## $Outcome
## [1] 0## [1] 0Since We cannot have Bps, BMI, Skin Thickness and Glucose being zero(0) all zero values were replaced with the mean/median since dropping them would change the data.
## Pregnancies Glucose BloodPressure SkinThickness
## Min. : 0.000 Min. : 44.00 Min. : 24.00 Min. : 7.00
## 1st Qu.: 1.000 1st Qu.: 99.75 1st Qu.: 64.00 1st Qu.:20.54
## Median : 3.000 Median :117.00 Median : 72.00 Median :23.00
## Mean : 3.845 Mean :121.68 Mean : 72.25 Mean :26.61
## 3rd Qu.: 6.000 3rd Qu.:140.25 3rd Qu.: 80.00 3rd Qu.:32.00
## Max. :17.000 Max. :199.00 Max. :122.00 Max. :99.00
## Insulin BMI DiabetesPedigreeFunction Age
## Min. : 14.00 Min. :18.20 Min. :0.0780 Min. :21.00
## 1st Qu.: 30.50 1st Qu.:27.50 1st Qu.:0.2437 1st Qu.:24.00
## Median : 31.25 Median :32.00 Median :0.3725 Median :29.00
## Mean : 94.65 Mean :32.45 Mean :0.4719 Mean :33.24
## 3rd Qu.:127.25 3rd Qu.:36.60 3rd Qu.:0.6262 3rd Qu.:41.00
## Max. :846.00 Max. :67.10 Max. :2.4200 Max. :81.00
## Outcome age_cat
## Min. :0.000 Length:768
## 1st Qu.:0.000 Class :character
## Median :0.000 Mode :character
## Mean :0.349
## 3rd Qu.:1.000
## Max. :1.000Insights
How was the population spread?
| age_cat | Percent | |||
|---|---|---|---|---|
| Below_30 | 51.56 | |||
| 30s | 21.48 | |||
| 40s | 15.36 | |||
| 50s | 7.42 | |||
| 60s | 3.78 | |||
| Above_70 | 0.39 |
Number of Pregnancies
The mean number of pregnancies was around 3 with the data being skewed to the left. A few extremes having above 11 pregnancies accounting for 4.43%
Comparison of pregnancies and outcome
Those who had higher number of pregnancies had a higher chance of being diabetic.
## Warning: Continuous x aesthetic
## ℹ did you forget `aes(group = ...)`?
BMI numbers
About 61.98 % of the population had a BMI of above 30 which is considered as being obese. 1.04% had a BMI of above 50 which even though possible could be an outlier. 0.52% percent of the population were underweight with a BMI < 18.5
## # A tibble: 9 × 2
## BMI_cat Percent
## <chr> <int>
## 1 Moderate Obese 235
## 2 Obese 179
## 3 Severe Obese 150
## 4 Normal 102
## 5 Very severe Obese 62
## 6 Morbid Obese 27
## 7 Super Obese 8
## 8 Underweight 4
## 9 Hyper obese 1
Diabetes pedigree Function and BMI
The super Obese, Severe Obese and very severe obese had the highest diabetes pedigree function. This in itself is not a good measure since even the underweight still have a high DPF
Glucose ranges
Those with Glucose above 125 classified as hypergylcemia accounted for 40.49 %, Impaired glucose had 34.5% while the hypoglycemia accounted for 1%
Glucose Ranges and Diabetes Pedigree Function
Both the hyperglycemia and impaired glucose had the highest diabetes pedigree function.
The patient with hyperglycemia or high glucose 2 hours after Oral glucose test had a higher chance of having diabetes as their outcome. Compared to the hypoglycemic who none had diabetes as the outcome.
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
How do the various paramaters relate to each other
comparison of age and outcome
Blood Pressure comparison
The study utilized Diastolic blood pressures that ranges from 60 - 80 mmHg. The BPs had a normal distribution though 11.2 % had low diastolic BP(less than 60mmHg). 62 % had normal BPs and 0.13% or 1 person had a Hypertensive crises BP > 120 .
Diastolic blood pressure did not have such high impact on the outcome for diabetes.
Recommendation
Since more than 60% of the population were obese education and lifestyle modification measures should introduced to the community.
More screening measures should be introduced. Gym services and also healthy eating should be adopted.