ML Use case 2 Diabetes Prediction

දෙවෙනි සම්පූර්ණ project එක, මෙවර structured medical data එකකින් — Rock vs Mine එකේ ම step 5 pattern එකම, ඒක එකපාරක් විතරක් නෙවෙයි කියලා, template එකක් කියලා ඔප්පු කරනවා.

A second full project, this time on structured medical data — the same five-step pattern as Rock vs Mine, proving it wasn't a one-off, it's a template.

8 May 20265 min readSelf Guide · Stage 13#Python & Data Basics#Hands-on Projects#Machine learning

Lesson 23 of 64 · Part 1 · Python & Data Basics · Hands-on Projects · bilingual notes (English + සිංහල)

Why this matters. A second full project, this time on structured medical data — the same five-step pattern as Rock vs Mine, proving it wasn't a one-off, it's a template.

සිංහලෙන්: දෙවෙනි සම්පූර්ණ project එක, මෙවර structured medical data එකකින් — Rock vs Mine එකේ ම step 5 pattern එකම, ඒක එකපාරක් විතරක් නෙවෙයි කියලා, template එකක් කියලා ඔප්පු කරනවා.

What you'll use this for. Health-screening tools that flag "high risk" patients for a doctor to review work on exactly this idea, at a larger scale.

සිංහලෙන්: "High risk" රෝගීන් doctor කෙනෙකුට review කරන්න flag කරන health-screening tools වැඩ කරන්නෙ, විශාල scale එකක, හරියටම මේ අදහසෙන්ම.

Key principles

More features doesn't automatically mean a better model. This dataset has just 8 medical features, yet gets solid accuracy — proving that clean, relevant features beat sheer quantity every time.
Features වැඩි වීම automatic ම හොඳ model එකක් කියන එකක් නෙවෙයි

Walkthrough

Importing the Dependencies

ExamplePython

import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn import svm
from sklearn.metrics import accuracy_score

Data Collection and Analysis

PIMA Diabetes Dataset

ExamplePython

# loading the diabetes dataset to a pandas DataFrame
diabetes_dataset = pd.read_csv('/content/diabetes.csv')

ExamplePython

pd.read_csv?

ExamplePython

# printing the first 5 rows of the dataset
diabetes_dataset.head()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	…
0	6	148	72	35	0	33.6	0.627	…
1	1	85	66	29	0	26.6	0.351	…
2	8	183	64	0	0	23.3	0.672	…
3	1	89	66	23	94	28.1	0.167	…
4	0	137	40	35	168	43.1	2.288	…

ExamplePython

# number of rows and Columns in this dataset
diabetes_dataset.shape

Output

Output

(768, 9)

ExamplePython

# getting the statistical measures of the data
diabetes_dataset.describe()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	…
count	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	…
std	3.369578	31.972618	19.355807	15.952218	115.244002	7.884160	0.331329	…
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.078000	…
25%	1.000000	99.000000	62.000000	0.000000	0.000000	27.300000	0.243750	…
50%	3.000000	117.000000	72.000000	23.000000	30.500000	32.000000	0.372500	…

… 2 more rows

ExamplePython

diabetes_dataset['Outcome'].value_counts()

Output

Output

0    500
1    268
Name: Outcome, dtype: int64

0 --> Non-Diabetic

1 --> Diabetic

ExamplePython

diabetes_dataset.groupby('Outcome').mean()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age
Outcome
0	3.298000	109.980000	68.184000	19.664000	68.792000	30.304200	0.429734	31.190000
1	4.865672	141.257463	70.824627	22.164179	100.335821	35.142537	0.550500	37.067164

ExamplePython

# separating the data and labels
X = diabetes_dataset.drop(columns = 'Outcome', axis=1)
Y = diabetes_dataset['Outcome']

ExamplePython

print(X)

Output

Output

     Pregnancies  Glucose  BloodPressure  ...   BMI  DiabetesPedigreeFunction  Age
0              6      148             72  ...  33.6                     0.627   50
1              1       85             66  ...  26.6                     0.351   31
2              8      183             64  ...  23.3                     0.672   32
3              1       89             66  ...  28.1                     0.167   21
4              0      137             40  ...  43.1                     2.288   33
..           ...      ...            ...  ...   ...                       ...  ...
763           10      101             76  ...  32.9                     0.171   63
764            2      122             70  ...  36.8                     0.340   27
765            5      121             72  ...  26.2                     0.245   30
766            1      126             60  ...  30.1                     0.349   47
767            1       93             70  ...  30.4                     0.315   23

[768 rows x 8 columns]

ExamplePython

print(Y)

Output

Output

0      1
1      0
2      1
3      0
4      1
      ..
763    0
764    0
765    0
766    1
767    0
Name: Outcome, Length: 768, dtype: int64

Data Standardization

ExamplePython

scaler = StandardScaler()

ExamplePython

scaler.fit(X)

Output

Output

StandardScaler(copy=True, with_mean=True, with_std=True)

ExamplePython

standardized_data = scaler.transform(X)

ExamplePython

print(standardized_data)

Output

Output

[[ 0.63994726  0.84832379  0.14964075 ...  0.20401277  0.46849198
   1.4259954 ]
 [-0.84488505 -1.12339636 -0.16054575 ... -0.68442195 -0.36506078
  -0.19067191]
 [ 1.23388019  1.94372388 -0.26394125 ... -1.10325546  0.60439732
  -0.10558415]
 ...
 [ 0.3429808   0.00330087  0.14964075 ... -0.73518964 -0.68519336
  -0.27575966]
 [-0.84488505  0.1597866  -0.47073225 ... -0.24020459 -0.37110101
   1.17073215]
 [-0.84488505 -0.8730192   0.04624525 ... -0.20212881 -0.47378505
  -0.87137393]]

ExamplePython

X = standardized_data
Y = diabetes_dataset['Outcome']

ExamplePython

print(X)
print(Y)

Output

Output

[[ 0.63994726  0.84832379  0.14964075 ...  0.20401277  0.46849198
   1.4259954 ]
 [-0.84488505 -1.12339636 -0.16054575 ... -0.68442195 -0.36506078
  -0.19067191]
 [ 1.23388019  1.94372388 -0.26394125 ... -1.10325546  0.60439732
  -0.10558415]
 ...
 [ 0.3429808   0.00330087  0.14964075 ... -0.73518964 -0.68519336
  -0.27575966]
 [-0.84488505  0.1597866  -0.47073225 ... -0.24020459 -0.37110101
   1.17073215]
 [-0.84488505 -0.8730192   0.04624525 ... -0.20212881 -0.47378505
  -0.87137393]]
0      1
1      0
2      1
3      0
4      1
… (7 more lines)

Train Test Split

ExamplePython

X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.2, stratify=Y, random_state=2)

ExamplePython

print(X.shape, X_train.shape, X_test.shape)

Output

Output

(768, 8) (614, 8) (154, 8)

Training the Model

ExamplePython

classifier = svm.SVC(kernel='linear')

ExamplePython

#training the support vector Machine Classifier
classifier.fit(X_train, Y_train)

Output

Output

SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,
    decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',
    max_iter=-1, probability=False, random_state=None, shrinking=True,
    tol=0.001, verbose=False)

Model Evaluation

Accuracy Score

ExamplePython

# accuracy score on the training data
X_train_prediction = classifier.predict(X_train)
training_data_accuracy = accuracy_score(X_train_prediction, Y_train)

ExamplePython

print('Accuracy score of the training data : ', training_data_accuracy)

Output

Output

Accuracy score of the training data :  0.7866449511400652

ExamplePython

# accuracy score on the test data
X_test_prediction = classifier.predict(X_test)
test_data_accuracy = accuracy_score(X_test_prediction, Y_test)

ExamplePython

print('Accuracy score of the test data : ', test_data_accuracy)

Output

Output

Accuracy score of the test data :  0.7727272727272727

Making a Predictive System

ExamplePython

input_data = (5,166,72,19,175,25.8,0.587,51)

# changing the input_data to numpy array
input_data_as_numpy_array = np.asarray(input_data)

# reshape the array as we are predicting for one instance
input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)

# standardize the input data
std_data = scaler.transform(input_data_reshaped)
print(std_data)

prediction = classifier.predict(std_data)
print(prediction)

if (prediction[0] == 0):
  print('The person is not diabetic')
else:
  print('The person is diabetic')

Output

Output

[[ 0.3429808   1.41167241  0.14964075 -0.09637905  0.82661621 -0.78595734
   0.34768723  1.51108316]]
[1]
The person is diabetic

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ML Use case 2 Diabetes Prediction

Key principles

Walkthrough

Importing the Dependencies

Data Standardization

Train Test Split

Training the Model

Model Evaluation

Accuracy Score

Making a Predictive System

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