Project 4 Heart Disease Prediction
ඔයාගෙ තුන්වෙනි සම්පූර්ණ project එක — වැරදි prediction එකක cost එක (ඇත්තටම heart disease තියෙන කෙනෙක් miss කරන එක) ඇත්තටම serious වන පළවෙනි එක, ඒ නිසා accuracy එකට වගේම evaluation එකටත් වැදගත්කම තියෙනවා.
Your third full project — and the first one where the cost of a wrong prediction (missing a real heart-disease case) is genuinely serious, which is why evaluation matters as much as accuracy here.
Lesson 29 of 64 · Part 2 · Maths & First Models · Hands-on Projects · bilingual notes (English + සිංහල)
Why this matters. Your third full project — and the first one where the cost of a wrong prediction (missing a real heart-disease case) is genuinely serious, which is why evaluation matters as much as accuracy here.
සිංහලෙන්: ඔයාගෙ තුන්වෙනි සම්පූර්ණ project එක — වැරදි prediction එකක cost එක (ඇත්තටම heart disease තියෙන කෙනෙක් miss කරන එක) ඇත්තටම serious වන පළවෙනි එක, ඒ නිසා accuracy එකට වගේම evaluation එකටත් වැදගත්කම තියෙනවා.
What you'll use this for. This is the pattern behind every real clinical risk-scoring tool you've heard of — take patient measurements in, output a risk level.
සිංහලෙන්: ඔයා අහල තියෙන සැබෑ clinical risk-scoring tool ඕනම එකක් වැඩ කරන pattern එකයි මේක — රෝගියෙකුගෙ measurements ඇතුලට, risk level එකක් එළියට.
Key principles
- In medical models, false negatives usually hurt more than false positives. Telling a sick patient they're healthy (a false negative) delays real treatment — which is why recall, not just accuracy, is often the metric that matters most here.
Medical models වලදී, false negatives සාමාන්‍යයෙන් false positives වලට වඩා හානිකරයි
Walkthrough
Importing the Dependencies
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
# loading the csv data to a Pandas DataFrame
heart_data = pd.read_csv('/content/data.csv')# 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 | … |
# 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 | … |
# number of rows and columns in the dataset
heart_data.shapeOutput
(303, 14)# getting some info about the data
heart_data.info()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)# checking for missing values
heart_data.isnull().sum()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: int64# 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
# checking the distribution of Target Variable
heart_data['target'].value_counts()Output
1 165
0 138
Name: target, dtype: int641 --> Defective Heart
0 --> Healthy Heart
Splitting the Features and Target
X = heart_data.drop(columns='target', axis=1)
Y = heart_data['target']print(X)Output
age sex cp trestbps chol ... exang oldpeak slope ca thal
0 63 1 3 145 233 ... 0 2.3 0 0 1
1 37 1 2 130 250 ... 0 3.5 0 0 2
2 41 0 1 130 204 ... 0 1.4 2 0 2
3 56 1 1 120 236 ... 0 0.8 2 0 2
4 57 0 0 120 354 ... 1 0.6 2 0 2
.. ... ... .. ... ... ... ... ... ... .. ...
298 57 0 0 140 241 ... 1 0.2 1 0 3
299 45 1 3 110 264 ... 0 1.2 1 0 3
300 68 1 0 144 193 ... 0 3.4 1 2 3
301 57 1 0 130 131 ... 1 1.2 1 1 3
302 57 0 1 130 236 ... 0 0.0 1 1 2
[303 rows x 13 columns]print(Y)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
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)print(X.shape, X_train.shape, X_test.shape)Output
(303, 13) (242, 13) (61, 13)Model Training
Logistic Regression
model = LogisticRegression()# training the LogisticRegression model with Training data
model.fit(X_train, Y_train)Output
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: 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
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=100,
multi_class='auto', n_jobs=None, penalty='l2',
random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
warm_start=False)Model Evaluation
Accuracy Score
# accuracy on training data
X_train_prediction = model.predict(X_train)
training_data_accuracy = accuracy_score(X_train_prediction, Y_train)print('Accuracy on Training data : ', training_data_accuracy)Output
Accuracy on Training data : 0.8512396694214877# accuracy on test data
X_test_prediction = model.predict(X_test)
test_data_accuracy = accuracy_score(X_test_prediction, Y_test)print('Accuracy on Test data : ', test_data_accuracy)Output
Accuracy on Test data : 0.819672131147541Building a Predictive System
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
[0]
The Person does not have a Heart DiseaseKeep going: ← Lesson 28 · Lesson 30 → · Track it in the Self Guide
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