Multiple disease prediction system - heart
Diabetes model එකේම packaging pattern එක, heart disease එකට apply කරනවා — model එකක් deploy කරන්න දැනගත්තම, ඊළඟ එක deploy කරන එක බොහොම විට repetition එකක් කියලා ශක්තිමත් කරනවා.
The same packaging pattern as the diabetes model, applied to heart disease — reinforcing that once you know how to deploy one model, deploying the next is mostly repetition.
19 May 20266 min readSelf Guide · Stage 17#Shipping Models to the World#Multiple Disease Prediction System#Machine learning
Lesson 55 of 64 · Part 5 · Shipping Models to the World · Multiple Disease Prediction System · bilingual notes (English + සිංහල)
Why this matters. The same packaging pattern as the diabetes model, applied to heart disease — reinforcing that once you know how to deploy one model, deploying the next is mostly repetition.
සිංහලෙන්: Diabetes model එකේම packaging pattern එක, heart disease එකට apply කරනවා — model එකක් deploy කරන්න දැනගත්තම, ඊළඟ එක deploy කරන එක බොහොම විට repetition එකක් කියලා ශක්තිමත් කරනවා.
What you'll use this for. This is one of three disease modules that get combined into a single multi-disease app — each one independent, but sharing the exact same deployment skeleton.
සිංහලෙන්: Multi-disease app එකකට combine වෙන disease modules තුනෙන් එකක් තමයි මේක — එකිනෙක independent, ඒත් හැම එකකම එකම deployment skeleton එකයි.
Key principles
- Reusable structure is what makes scaling to more diseases realistic. Because the loading, input, and prediction logic follow the same shape as the diabetes app, adding heart disease took far less new code than the first module did.
Reusable structure එකයි, තව රෝග වලට scale කරන එක realistic කරන්නෙ
Walkthrough
Importing the Dependencies
ExamplePython
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_scoreData Collection and Processing
ExamplePython
# loading the csv data to a Pandas DataFrame
heart_data = pd.read_csv('/content/heart.csv')ExamplePython
# print first 5 rows of the dataset
heart_data.head()| age | sex | cp | trestbps | chol | fbs | restecg | … | |
|---|---|---|---|---|---|---|---|---|
| 0 | 63 | 1 | 3 | 145 | 233 | 1 | 0 | … |
| 1 | 37 | 1 | 2 | 130 | 250 | 0 | 1 | … |
| 2 | 41 | 0 | 1 | 130 | 204 | 0 | 0 | … |
| 3 | 56 | 1 | 1 | 120 | 236 | 0 | 1 | … |
| 4 | 57 | 0 | 0 | 120 | 354 | 0 | 1 | … |
ExamplePython
# print last 5 rows of the dataset
heart_data.tail()| age | sex | cp | trestbps | chol | fbs | restecg | … | |
|---|---|---|---|---|---|---|---|---|
| 298 | 57 | 0 | 0 | 140 | 241 | 0 | 1 | … |
| 299 | 45 | 1 | 3 | 110 | 264 | 0 | 1 | … |
| 300 | 68 | 1 | 0 | 144 | 193 | 1 | 1 | … |
| 301 | 57 | 1 | 0 | 130 | 131 | 0 | 1 | … |
| 302 | 57 | 0 | 1 | 130 | 236 | 0 | 0 | … |
ExamplePython
# number of rows and columns in the dataset
heart_data.shapeOutput
Output
(303, 14)ExamplePython
# getting some info about the data
heart_data.info()Output
Output
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 303 entries, 0 to 302
Data columns (total 14 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 age 303 non-null int64
1 sex 303 non-null int64
2 cp 303 non-null int64
3 trestbps 303 non-null int64
4 chol 303 non-null int64
5 fbs 303 non-null int64
6 restecg 303 non-null int64
7 thalach 303 non-null int64
8 exang 303 non-null int64
9 oldpeak 303 non-null float64
10 slope 303 non-null int64
11 ca 303 non-null int64
12 thal 303 non-null int64
… (3 more lines)ExamplePython
# checking for missing values
heart_data.isnull().sum()Output
Output
age 0
sex 0
cp 0
trestbps 0
chol 0
fbs 0
restecg 0
thalach 0
exang 0
oldpeak 0
slope 0
ca 0
thal 0
target 0
dtype: int64ExamplePython
# statistical measures about the data
heart_data.describe()| age | sex | cp | trestbps | chol | fbs | restecg | … | |
|---|---|---|---|---|---|---|---|---|
| count | 303.000000 | 303.000000 | 303.000000 | 303.000000 | 303.000000 | 303.000000 | 303.000000 | … |
| mean | 54.366337 | 0.683168 | 0.966997 | 131.623762 | 246.264026 | 0.148515 | 0.528053 | … |
| std | 9.082101 | 0.466011 | 1.032052 | 17.538143 | 51.830751 | 0.356198 | 0.525860 | … |
| min | 29.000000 | 0.000000 | 0.000000 | 94.000000 | 126.000000 | 0.000000 | 0.000000 | … |
| 25% | 47.500000 | 0.000000 | 0.000000 | 120.000000 | 211.000000 | 0.000000 | 0.000000 | … |
| 50% | 55.000000 | 1.000000 | 1.000000 | 130.000000 | 240.000000 | 0.000000 | 1.000000 | … |
… 2 more rows
ExamplePython
# checking the distribution of Target Variable
heart_data['target'].value_counts()Output
Output
1 165
0 138
Name: target, dtype: int641 --> Defective Heart
0 --> Healthy Heart
Splitting the Features and Target
ExamplePython
X = heart_data.drop(columns='target', axis=1)
Y = heart_data['target']ExamplePython
print(X)Output
Output
age sex cp trestbps chol fbs restecg thalach exang oldpeak \
0 63 1 3 145 233 1 0 150 0 2.3
1 37 1 2 130 250 0 1 187 0 3.5
2 41 0 1 130 204 0 0 172 0 1.4
3 56 1 1 120 236 0 1 178 0 0.8
4 57 0 0 120 354 0 1 163 1 0.6
.. ... ... .. ... ... ... ... ... ... ...
298 57 0 0 140 241 0 1 123 1 0.2
299 45 1 3 110 264 0 1 132 0 1.2
300 68 1 0 144 193 1 1 141 0 3.4
301 57 1 0 130 131 0 1 115 1 1.2
302 57 0 1 130 236 0 0 174 0 0.0
slope ca thal
0 0 0 1
1 0 0 2
2 2 0 2
3 2 0 2
… (9 more lines)ExamplePython
print(Y)Output
Output
0 1
1 1
2 1
3 1
4 1
..
298 0
299 0
300 0
301 0
302 0
Name: target, Length: 303, dtype: int64Splitting the Data into Training data & Test Data
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
(303, 13) (242, 13) (61, 13)Model Training
Logistic Regression
ExamplePython
model = LogisticRegression()ExamplePython
# training the LogisticRegression model with Training data
model.fit(X_train, Y_train)Output
Output
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,Output
Output
LogisticRegression()Model Evaluation
Accuracy Score
ExamplePython
# accuracy on training data
X_train_prediction = model.predict(X_train)
training_data_accuracy = accuracy_score(X_train_prediction, Y_train)ExamplePython
print('Accuracy on Training data : ', training_data_accuracy)Output
Output
Accuracy on Training data : 0.8512396694214877ExamplePython
# accuracy on test data
X_test_prediction = model.predict(X_test)
test_data_accuracy = accuracy_score(X_test_prediction, Y_test)ExamplePython
print('Accuracy on Test data : ', test_data_accuracy)Output
Output
Accuracy on Test data : 0.819672131147541Building a Predictive System
ExamplePython
input_data = (62,0,0,140,268,0,0,160,0,3.6,0,2,2)
# change the input data to a numpy array
input_data_as_numpy_array= np.asarray(input_data)
# reshape the numpy array as we are predicting for only on instance
input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)
prediction = model.predict(input_data_reshaped)
print(prediction)
if (prediction[0]== 0):
print('The Person does not have a Heart Disease')
else:
print('The Person has Heart Disease')Output
Output
[0]
The Person does not have a Heart DiseaseOutput
Output
/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature nam
"X does not have valid feature names, but"Saving the trained model
ExamplePython
import pickleExamplePython
filename = 'heart_disease_model.sav'
pickle.dump(model, open(filename, 'wb'))ExamplePython
# loading the saved model
loaded_model = pickle.load(open('heart_disease_model.sav', 'rb'))ExamplePython
for column in X.columns:
print(column)Output
Output
age
sex
cp
trestbps
chol
fbs
restecg
thalach
exang
oldpeak
slope
ca
thalKeep going: ← Lesson 54 · Lesson 56 → · Track it in the Self Guide
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