Heart Disease Prediction

 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.