Pima Indian Diabetes Classification with EDA

Pima Indian diabetes classification

Table of Contents¶

Data Preprocessing-
- 1.1 Importing libraries
- 1.2 Data Preprocessing
Exploratory Data Analysis-
- 2.1 Univariate Analysis
- 2.2 Bivariate Analysis
Classification-
- 3.1 Splitting the dataset
- 3.2 Decision Tree Classifier
- 3.3 Naive Bayes(Gaussian NB)
- 3.4 Logistic Regression
- 3.5 Random Forest Classifer
- 3.6 Support Vector Machine
- 3.7 K-nearest Neighbors Classifer
Model Evaluation-
- 4.1 Decision Tree
- 4.2 Naive Bayes(Gaussian NB)
- 4.3 Logistic Regressio
- 4.4 Random Forest
- 4.5 Support Vector Machine
- 4.6 K-nearest Neighbors
- 4.7 Comparision of Accuracies
Conclusion

About the Dataset¶

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. 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. The datasets consists of several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and so on.

Dataset Link - https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database¶

What is diabetes ?¶

Acccording to NIH, "Diabetes is a disease that occurs when your blood glucose, also called blood sugar, is too high. Blood glucose is your main source of energy and comes from the food you eat. Insulin, a hormone made by the pancreas, helps glucose from food get into your cells to be used for energy. Sometimes your body doesn’t make enough—or any—insulin or doesn’t use insulin well. Glucose then stays in your blood and doesn’t reach your cells.

Over time, having too much glucose in your blood can cause health problems. Although diabetes has no cure, you can take steps to manage your diabetes and stay healthy.

Sometimes people call diabetes “a touch of sugar” or “borderline diabetes.” These terms suggest that someone doesn’t really have diabetes or has a less serious case, but every case of diabetes is serious.

What are the different types of diabetes? The most common types of diabetes are type 1, type 2, and gestational diabetes.

Type 1 diabetes If you have type 1 diabetes, your body does not make insulin. Your immune system attacks and destroys the cells in your pancreas that make insulin. Type 1 diabetes is usually diagnosed in children and young adults, although it can appear at any age. People with type 1 diabetes need to take insulin every day to stay alive.
Type 2 diabetes If you have type 2 diabetes, your body does not make or use insulin well. You can develop type 2 diabetes at any age, even during childhood. However, this type of diabetes occurs most often in middle-aged and older people. Type 2 is the most common type of diabetes.
Gestational diabetes Gestational diabetes develops in some women when they are pregnant. Most of the time, this type of diabetes goes away after the baby is born. However, if you’ve had gestational diabetes, you have a greater chance of developing type 2 diabetes later in life. Sometimes diabetes diagnosed during pregnancy is actually type 2 diabetes.

Other types of diabetes Less common types include monogenic diabetes, which is an inherited form of diabetes, and cystic fibrosis-related diabetes ."

Data Dictionary¶

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
Age - Age (years)
Outcome - it consists of data whether person is diabetec(1) or healthy(0) `

GLUCOSE - glucose is a type of sugar that gives our bodies the energy to function. In diabetes, the glucose level in the blood becomes too high, and it needs to be managed carefully to avoid health complications.

Blood Pressure - Having high blood pressure when you already have diabetes can worsen the diabetes-related complications. High blood pressure puts extra stress on the heart and blood vessels, increasing the risk of heart disease, stroke, kidney problems, and eye issues.

Skin Thickness - Skin thickness refers to the measurement of the thickness of the skin, which is the outer covering of the human body. The skin is the largest organ in the body and plays a vital role in protecting the underlying tissues, regulating body temperature, and providing sensory information to the brain.

Insulin - Insulin is a hormone produced by a gland in the body called the pancreas. When we eat food, especially carbohydrates, our body breaks down the food into glucose, which is a type of sugar. Glucose is the primary source of energy for our cells, including those in our muscles and organs.

DiabetesPedigreeFunction - the Diabetes Pedigree Function is a tool used to estimate the genetic risk of type 2 diabetes based on the family history of the disease. It is not a direct measure of diabetes management or treatment but can be helpful in identifying individuals at higher risk, prompting early preventive measures and health monitoring.

Who is Pima Indians ?¶

"The Pima (or Akimel O'odham, also spelled Akimel O'otham, "River People", formerly known as Pima) are a group of Native Americans living in an area consisting of what is now central and southern Arizona. The majority population of the surviving two bands of the Akimel O'odham are based in two reservations: the Keli Akimel O'otham on the Gila River Indian Community (GRIC) and the On'k Akimel O'odham on the Salt River Pima-Maricopa Indian Community (SRPMIC)." Wikipedia

1.1 Importing required libraries¶

In [1]:

#data Manipulation
import pandas as pd

#Mathematical operation
import numpy as np

#data Visualization
import matplotlib.pyplot as plt
import seaborn as sns


#Remove Warnings
import warnings
warnings.filterwarnings('ignore')

#Ml Algoriothm
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_score,classification_report
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn import svm
from sklearn.metrics import accuracy_score,mean_absolute_error,mean_squared_error,r2_score,confusion_matrix

1.2 Data Preprocessing¶

In the data preprocessing phase we first examined the shape of the data to understand its dimension
Next, We checked for null values in the dataset and removed if any were found
Additionally, we performed a check for duplicate values and removed them to ensure data integrity
To gain insights into relationships between different variables we performed univariate,bivariate and multivariate analysis and then visualized the correlation map using a heatmap. This visualization allowed us to identify the patterns and dependencies among the features in the dataset.

In [2]:

#Load the Dataset
data = pd.read_csv(r"C:\Users\Lenovo\Documents\jupyter\DataSets\diabetes.csv")
data.sample(10)

# Creating copy of actual dataframe
df=data.copy()

In [3]:

#check for shape
data.shape

Out[3]:

(768, 9)

From above cell we see that there are 768 observation and 9 features in our data

In [4]:

#check for columns
data.columns

Out[4]:

Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',
       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],
      dtype='object')

In [5]:

# check info of each column
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 768 entries, 0 to 767
Data columns (total 9 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Pregnancies               768 non-null    int64  
 1   Glucose                   768 non-null    int64  
 2   BloodPressure             768 non-null    int64  
 3   SkinThickness             768 non-null    int64  
 4   Insulin                   768 non-null    int64  
 5   BMI                       768 non-null    float64
 6   DiabetesPedigreeFunction  768 non-null    float64
 7   Age                       768 non-null    int64  
 8   Outcome                   768 non-null    int64  
dtypes: float64(2), int64(7)
memory usage: 54.1 KB

From above cell we see that there are two column contain float values and 7 column contain integer value also there are no missing values in our data

In [6]:

#check for duplicate value
data.duplicated().sum()

Out[6]:

From above cell we see that there are no duplicate values in our data

In [7]:

# summary statistics of numerical columns
data.describe()

Out[7]:

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
count	768.000000	768.000000	768.000000	768.000000	768.000000	768.000000	768.000000	768.000000	768.000000
mean	3.845052	120.894531	69.105469	20.536458	79.799479	31.992578	0.471876	33.240885	0.348958
std	3.369578	31.972618	19.355807	15.952218	115.244002	7.884160	0.331329	11.760232	0.476951
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.078000	21.000000	0.000000
25%	1.000000	99.000000	62.000000	0.000000	0.000000	27.300000	0.243750	24.000000	0.000000
50%	3.000000	117.000000	72.000000	23.000000	30.500000	32.000000	0.372500	29.000000	0.000000
75%	6.000000	140.250000	80.000000	32.000000	127.250000	36.600000	0.626250	41.000000	1.000000
max	17.000000	199.000000	122.000000	99.000000	846.000000	67.100000	2.420000	81.000000	1.000000

2.1 Univariate Analysis¶

univariate analysis is the first step in statistical analysis, providing a foundation for understanding the properties of individual variables before moving on to more complex analyses involving multiple variables.

Pregnancies¶

In [8]:

ax=sns.countplot(x='Pregnancies',data=data,palette='magma')
for i in ax.containers:
    ax.bar_label(i)
plt.title("Pregnancies")
plt.show()

Glucose¶

In [9]:

sns.displot(x='Glucose',data=data,kind='hist',kde=True,palette='Set1')
plt.title('Distribution of Glucose')
plt.show()

From above chart we see that there are some people whose glucose level is 0 mg/dl that is not possible.

so we can say that we have incorrect information of these people.

now let's remove this incorrect data

In [10]:

#Let's check incorrect data 
data[data['Glucose']==0]

Out[10]:

	Pregnancies	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
75	1	48	20	0	24.7	0.140	22	0
182	1	74	20	23	27.7	0.299	21	0
342	1	68	35	0	32.0	0.389	22	0
349	5	80	32	0	41.0	0.346	37	1
502	6	68	41	0	39.0	0.727	41	1

From above data we can see that above data is incorrect beacause these people's have insulin level is also 0 mu that is also not possible, so let's drop these rows

In [11]:

x=data[data['Glucose']==0].index.to_list()
data.drop(index=x,inplace=True)

In [12]:

fig, axes = plt.subplots(nrows=1, ncols=2,figsize=(12,5))

sns.histplot(df['Glucose'],kde=True,ax=axes[0])
sns.histplot(data['Glucose'],kde=True,ax=axes[1])
axes[0].set_title('Distribution of Glucose before replacing')
axes[1].set_title('Distribution of Glucose after replacing')

plt.show()

Blood Pressure¶

In [13]:

sns.displot(x='BloodPressure',data=data,kind='hist',kde=True)
plt.title('Distribution of Blood Pressure')
plt.show()

From above distribution plot we see that there are a few people whose blood pressure is 0 mm(hg) that is also not possible

In medical practice, a blood pressure reading of 0 mm(hg) would typically indicate that the blood pressure measurement was not taken correctly or that there is a significant error in the measurement process. In real-life situations, blood pressure levels below normal (hypotension) can occur, but they would not reach zero. Severe hypotension can lead to fainting, dizziness, and other health problems.
so we can say that there are also incorrect data in our BloodPressure column.
now we have two option either we can remove this data or we can replace this incorrect data
Firstly we have to check that incorrect data

In [14]:

#check incorrect data
print("Shape of incorrect data:", data[data['BloodPressure']==0].shape)
data[data['BloodPressure']==0]

Shape of incorrect data: (35, 9)

Out[14]:

	Pregnancies	Glucose	SkinThickness	BMI	DiabetesPedigreeFunction	Age	Outcome
7	10	115	0	35.3	0.134	29	0
15	7	100	0	30.0	0.484	32	1
49	7	105	0	0.0	0.305	24	0
60	2	84	0	0.0	0.304	21	0
78	0	131	0	43.2	0.270	26	1
81	2	74	0	0.0	0.102	22	0
172	2	87	23	28.9	0.773	25	0
193	11	135	0	52.3	0.578	40	1
222	7	119	0	25.2	0.209	37	0
261	3	141	0	30.0	0.761	27	1
266	0	138	0	36.3	0.933	25	1
269	2	146	0	27.5	0.240	28	1
300	0	167	0	32.3	0.839	30	1
332	1	180	0	43.3	0.282	41	1
336	0	117	0	33.8	0.932	44	0
347	3	116	0	23.5	0.187	23	0
357	13	129	30	39.9	0.569	44	1
426	0	94	0	0.0	0.256	25	0
430	2	99	0	22.2	0.108	23	0
435	0	141	0	42.4	0.205	29	1
453	2	119	0	19.6	0.832	72	0
468	8	120	0	30.0	0.183	38	1
484	0	145	0	44.2	0.630	31	1
494	3	80	0	0.0	0.174	22	0
522	6	114	0	0.0	0.189	26	0
533	6	91	0	29.8	0.501	31	0
535	4	132	0	32.9	0.302	23	1
589	0	73	0	21.1	0.342	25	0
601	6	96	0	23.7	0.190	28	0
604	4	183	0	28.4	0.212	36	1
619	0	119	0	32.4	0.141	24	1
643	4	90	0	28.0	0.610	31	0
697	0	99	0	25.0	0.253	22	0
703	2	129	0	38.5	0.304	41	0
706	10	115	0	0.0	0.261	30	1

From above we see that there are 35 observations with incorrect data in Glucose column

Let's replace this data with mean of BloodPressure column
Why i choose mean to replace 0:
- Because we have limited data and removing observations with incorrect data might lead to a loss of useful information
- because the slope is high and data's distribution is normal
- generally the blood Pressure of people is around 70 - 80 and mean of the data is also in between 70 - 80 so we replace with mean instead of median.

In [15]:

data['BloodPressure'].replace(0,np.nan,inplace=True)

data['BloodPressure'].fillna(data['BloodPressure'].mean(skipna=True),inplace=True)

In [16]:

fig, axes = plt.subplots(nrows=1, ncols=2,figsize=(12,5))

sns.histplot(df['BloodPressure'],kde=True,ax=axes[0])
sns.histplot(data['BloodPressure'],kde=True,ax=axes[1])
axes[0].set_title('Distribution of Blood Pressure before replacing')
axes[1].set_title('Distribution of Blood Pressure after replacing')

plt.show()