Heart Disease Prediction
1. Introduction
Objective: Build a machine learning model to predict the likelihood of heart
disease based on health features.
Purpose: Aid healthcare providers in identifying high-risk patients and taking
preventative measures.
2. Project Workflow
1. Problem Definition:
- Predict the presence of heart
disease based on health indicators.
- Key questions:
- What features most strongly
indicate heart disease?
- How accurately can we classify
heart disease risk?
2. Data Collection:
- Source: Public datasets like the UCI
Heart Disease Dataset.
- Example features: Age, Gender,
Cholesterol, Blood Pressure, Chest Pain Type, Max Heart Rate.
3. Data Preprocessing:
- Handle missing values, outliers, and
encode categorical variables.
4. Model Development:
- Use classification algorithms like
Logistic Regression, Random Forest, or Support Vector Machine.
5. Model Evaluation:
- Evaluate model performance using
accuracy, precision, recall, and F1-score.
3. Technical Requirements
- Programming Language: Python
- Libraries/Tools:
- Data Handling: Pandas, NumPy
- Data Visualization: Matplotlib,
Seaborn
- Classification Models: Scikit-learn
- Model Evaluation: Scikit-learn
4. Implementation Steps
Step 1: Setup Environment
Install required libraries:
```
pip install pandas numpy matplotlib seaborn scikit-learn
```
Step 2: Load and Explore Data
Load the heart disease dataset:
```
import pandas as pd
data = pd.read_csv("heart_disease_data.csv")
print(data.head())
```
Explore key statistics and visualize feature distributions:
```
print(data.describe())
import seaborn as sns
sns.pairplot(data, hue='HeartDisease')
```
Step 3: Preprocess Data
Handle missing values and encode categorical data:
```
data = data.dropna() # Drop rows with
missing values
# Encode categorical variables
data = pd.get_dummies(data, drop_first=True)
```
Normalize numerical features:
```
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaled_features = scaler.fit_transform(data.drop('HeartDisease', axis=1))
data_scaled = pd.DataFrame(scaled_features, columns=data.columns[:-1])
data_scaled['HeartDisease'] = data['HeartDisease']
```
Step 4: Build Classification Model
Split the data into training and testing sets:
```
from sklearn.model_selection import train_test_split
X = data_scaled.drop('HeartDisease', axis=1)
y = data_scaled['HeartDisease']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)
```
Train a Logistic Regression model:
```
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)
```
Predict on the test set:
```
y_pred = model.predict(X_test)
```
Step 5: Evaluate Model
Evaluate the model using classification metrics:
```
from sklearn.metrics import accuracy_score, precision_score, recall_score,
f1_score
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)
print("Accuracy:", accuracy)
print("Precision:", precision)
print("Recall:", recall)
print("F1-Score:", f1)
```
Visualize a confusion matrix:
```
from sklearn.metrics import ConfusionMatrixDisplay
ConfusionMatrixDisplay.from_predictions(y_test, y_pred)
```
Step 6: Advanced Modeling (Optional)
Experiment with other classification algorithms:
- Random Forest Classifier
- Support Vector Machine
Example:
```
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier(random_state=42)
rf_model.fit(X_train, y_train)
rf_pred = rf_model.predict(X_test)
```
5. Expected Outcomes
1. A trained classification model capable of predicting heart disease risk.
2. Insights into the most significant health features affecting heart disease.
3. Model evaluation metrics for assessing prediction accuracy and reliability.
6. Additional Suggestions
- Deployment:
- Develop a web-based tool for
clinicians to input patient data and get predictions.
- Use frameworks like Flask or
Streamlit for deployment.
- Feature Importance:
- Use feature importance scores to
provide interpretability.
- Continuous Learning:
- Retrain the model periodically with
updated patient data for improved accuracy.