Predicting heart disease using machine learning.

This posts describe the methodology of a classification machine learning problem. For this purpose used Python based machine learning and data science libraries to build a ml model and predict whether or not someone has heart disease based on their medical attributes.

We’re going to take the following approach:
1. Problem definition
2. Data
3. Evaluation
4. Features
5. Modelling
6. Exprerimentation

1. Problem Definition:

Can we predict whether or not a person has heart disease from given clinical parameters?

2. Data:

The data is from kaggle.

3. Evaluation:

To reach about 95% accurancy at predicting whether or not a patient has heart disease.

4. Features:

1. age – age in years
2. sex – (1 = male; 0 = female)
3. cp – chest pain type
0: Typical angina: chest pain related decrease blood supply to the heart
1: Atypical angina: chest pain not related to heart
2: Non-anginal pain: typically esophageal spasms (non heart related)
3: Asymptomatic: chest pain not showing signs of disease

4. trestbps – resting blood pressure (in mm Hg on admission to the hospital)
anything above 130-140 is typically cause for concern
5. chol – serum cholestoral in mg/dl
6. serum = LDL + HDL + .2 * triglycerides
above 200 is cause for concern
fbs – (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)
‘>126’ mg/dL signals diabetes

7. restecg – resting electrocardiographic results
0: Nothing to note
1: ST-T Wave abnormality
can range from mild symptoms to severe problems signals non-normal heart beat
2: Possible or definite left ventricular hypertrophy Enlarged heart’s main pumping chamber
8. thalach – maximum heart rate achieved
9. exang – exercise induced angina (1 = yes; 0 = no)
oldpeak – ST depression induced by exercise relative to rest
looks at stress of heart during excercise
unhealthy heart will stress more
11. slope – the slope of the peak exercise ST segment
0: Upsloping: better heart rate with excercise (uncommon)
1: Flatsloping: minimal change (typical healthy heart)
2: Downslopins: signs of unhealthy heart
12 ca – number of major vessels (0-3) colored by flourosopy
colored vessel means the doctor can see the blood passing through
the more blood movement the better (no clots)
13. thal – thalium stress result
1,3: normal
6: fixed defect: used to be defect but ok now
7: reversable defect: no proper blood movement when excercising
14. target – have disease or not (1=yes, 0=no) (= the predicted attribute)


Some code..
# Import all the tools we need

# Regular EDA (exploratory data analysis) and plotting libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# we want our plots to appea r inside the notebook
%matplotlib inline 

# Models from Scikit-Learn
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier

# Model Evaluatiosn
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.model_selection import RandomizedSearchCV, GridSearchCV
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.metrics import precision_score, recall_score, f1_score
from sklearn.metrics import RocCurveDisplay # new in Scikit-Learn 1.2+

Heart Disease

Predicting heart disease using machine learning.