Machine Learning Model For Predicting Heart Disease
In this blog, I’ll share my experience and method to Build a Machine Learning Model for predicting Heart Disease.
Dataset Used- https://www.kaggle.com/ronitf/heart-disease-uci
About the dataset:
Attribute Information:
Only 14 attributes used:
1. #3 (age)
2. #4 (sex)
3. #9 (cp)
4. #10 (trestbps)
5. #12 (chol)
6. #16 (fbs)
7. #19 (restecg)
8. #32 (thalach)
9. #38 (exang)
10. #40 (oldpeak)
11. #41 (slope)
12. #44 (ca)
13. #51 (thal)
14. #58 (num) (the predicted attribute)
Complete attribute documentation:
1 id: patient identification number
2 ccf: social security number (I replaced this with a dummy value of 0)
3 age: age in years
4 sex: sex (1 = male; 0 = female)
5 painloc: chest pain location (1 = substernal; 0 = otherwise)
6 painexer (1 = provoked by exertion; 0 = otherwise)
7 relrest (1 = relieved after rest; 0 = otherwise)
8 pncaden (sum of 5, 6, and 7)
9 cp: chest pain type
— Value 1: typical angina
— Value 2: atypical angina
— Value 3: non-anginal pain
— Value 4: asymptomatic
10 trestbps: resting blood pressure (in mm Hg on admission to the hospital)
11 htn
12 chol: serum cholestoral in mg/dl
13 smoke: I believe this is 1 = yes; 0 = no (is or is not a smoker)
14 cigs (cigarettes per day)
15 years (number of years as a smoker)
16 fbs: (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)
17 dm (1 = history of diabetes; 0 = no such history)
18 famhist: family history of coronary artery disease (1 = yes; 0 = no)
19 restecg: resting electrocardiographic results
— Value 0: normal
— Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)
— Value 2: showing probable or definite left ventricular hypertrophy by Estes’ criteria
20 ekgmo (month of exercise ECG reading)
21 ekgday(day of exercise ECG reading)
22 ekgyr (year of exercise ECG reading)
23 dig (digitalis used furing exercise ECG: 1 = yes; 0 = no)
24 prop (Beta blocker used during exercise ECG: 1 = yes; 0 = no)
25 nitr (nitrates used during exercise ECG: 1 = yes; 0 = no)
26 pro (calcium channel blocker used during exercise ECG: 1 = yes; 0 = no)
27 diuretic (diuretic used used during exercise ECG: 1 = yes; 0 = no)
28 proto: exercise protocol
1 = Bruce
2 = Kottus
3 = McHenry
4 = fast Balke
5 = Balke
6 = Noughton
7 = bike 150 kpa min/min (Not sure if “kpa min/min” is what was written!)
8 = bike 125 kpa min/min
9 = bike 100 kpa min/min
10 = bike 75 kpa min/min
11 = bike 50 kpa min/min
12 = arm ergometer
29 thaldur: duration of exercise test in minutes
30 thaltime: time when ST measure depression was noted
31 met: mets achieved
32 thalach: maximum heart rate achieved
33 thalrest: resting heart rate
34 tpeakbps: peak exercise blood pressure (first of 2 parts)
35 tpeakbpd: peak exercise blood pressure (second of 2 parts)
36 dummy
37 trestbpd: resting blood pressure
38 exang: exercise induced angina (1 = yes; 0 = no)
39 xhypo: (1 = yes; 0 = no)
40 oldpeak = ST depression induced by exercise relative to rest
41 slope: the slope of the peak exercise ST segment
— Value 1: upsloping
— Value 2: flat
— Value 3: downsloping
42 rldv5: height at rest
43 rldv5e: height at peak exercise
44 ca: number of major vessels (0–3) colored by flourosopy
45 restckm: irrelevant
46 exerckm: irrelevant
47 restef: rest raidonuclid (sp?) ejection fraction
48 restwm: rest wall (sp?) motion abnormality
0 = none
1 = mild or moderate
2 = moderate or severe
3 = akinesis or dyskmem (sp?)
49 exeref: exercise radinalid (sp?) ejection fraction
50 exerwm: exercise wall (sp?) motion
51 thal: 3 = normal; 6 = fixed defect; 7 = reversable defect
52 thalsev: not used
53 thalpul: not used
54 earlobe: not used
55 cmo: month of cardiac cath (sp?) (perhaps “call”)
56 cday: day of cardiac cath (sp?)
57 cyr: year of cardiac cath (sp?)
58 num: diagnosis of heart disease (angiographic disease status)
— Value 0: < 50% diameter narrowing
— Value 1: > 50% diameter narrowing
(in any major vessel: attributes 59 through 68 are vessels)
59 lmt
60 ladprox
61 laddist
62 diag
63 cxmain
64 ramus
65 om1
66 om2
67 rcaprox
68 rcadist
69 lvx1: not used
70 lvx2: not used
71 lvx3: not used
72 lvx4: not used
73 lvf: not used
74 cathef: not used
75 junk: not used
76 name: last name of patient (I replaced this with the dummy string “name”)
Steps to be followed while creating a Heart Disease Model are: —
- Load the necessary libraries.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.cm import rainbow
from matplotlib import rcParams
%matplotlib inline
import warnings
warnings.filterwarnings(‘ignore’)
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import cross_val_score
from sklearn.metrics import confusion_matrix#importing 3 different classifiers KNeighborsClassifier, DecisionTreeClassifier, RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
2. Get the dataset description
df.describe()
3. Get the dataset information
df.info()
4. Perform some Data Preprocessing and StandardScaler
#Data Preprocessing.
dataset = pd.get_dummies(df, columns = [‘sex’, ‘cp’, ‘fbs’, ‘restecg’, ‘exang’, ‘slope’, ‘ca’, ‘thal’])standardScaler = StandardScaler()
columns_to_scale = [‘age’, ‘trestbps’, ‘chol’, ‘thalach’, ‘oldpeak’]
dataset[columns_to_scale] = standardScaler.fit_transform(dataset[columns_to_scale])
5. Fix your data in X and y.
y = dataset[‘target’]
X = dataset.drop([‘target’], axis = 1)Here y-axis contains target data and X-axis contains rest all the features.
6. Now let’s split the dataset into training and testing for this we will use train_test_split library.
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)Here I have kept 25% for testing and the rest 75% is for training the model.
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7. Now decide the model and try to fit the dataset into it. Here we are going to use KNN classifier to classify the data.
knn_classifier = KNeighborsClassifier(n_neighbors = 5, metric = ‘minkowski’, p = 2)
knn_classifier.fit(X_train, y_train)
y_pred_knn=knn_classifier.predict(X_test)
knn_classifier.score(X_test,y_test)OUTPUT
88.15% Accuracy.
Our accuracy is 88.15%. Our accuracy is good. Try out the dataset with different classifier model and comment me your accuracy.
To view my GitHub repository Click here
I’ll try the same dataset with TensorFlow also and soon post the blog for that too. So follow me so that you can get my blog updates regularly.
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