Deep Learning Project – Heart Disease Predictor using ANN Part – 2

Program 1

Heart Disease Dataset

Heart Disease Predictor using ANN (Keras)

We want to predict whether a person has heart disease or not based on multiple health parameters. This is a binary classification problem (0 = No disease, 1 = Disease).
| Column   | Description                                             |
| -------- | ------------------------------------------------------- |
| age      | Age                                                     |
| sex      | 1 = male; 0 = female                                    |
| cp       | Chest pain type (0-3)                                   |
| trestbps | Resting blood pressure                                  |
| chol     | Serum cholesterol                                       |
| fbs      | Fasting blood sugar (> 120 mg/dl) (1 = true; 0 = false) |
| restecg  | Resting electrocardiographic results                    |
| thalach  | Maximum heart rate achieved                             |
| exang    | Exercise induced angina                                 |
| oldpeak  | ST depression induced by exercise                       |
| slope    | Slope of the peak exercise ST segment                   |
| ca       | Major vessels (0-3) colored by fluoroscopy              |
| thal     | 1 = normal; 2 = fixed defect; 3 = reversible defect     |
| target   | 0 = no disease, 1 = disease                             |


# Import Libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import confusion_matrix, classification_report
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

#  Load Dataset
df = pd.read_csv("D://scikit_data/heart/heart.csv")
df.isnull().sum()

# Data Preprocessing

# Split features and target
X = df.drop('target', axis=1)
y = df['target']
X.head()
y.head()

df.shape

# Scale features between 0 and 1
scaler = MinMaxScaler()
X_scaled = scaler.fit_transform(X)

# Split into train-test
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
len(X_train)
len(X_test)

#  Build ANN Model
model = Sequential()
# Input layer
model.add(Dense(32, activation='relu', input_shape=(X.shape[1],))) # Input + First Hidden
# Hidden layers
model.add(Dense(16, activation='relu')) # Second Hidden
model.add(Dense(8, activation='relu')) # Thrid Hidden
# Output layer (binary classification)
model.add(Dense(1, activation='sigmoid')) # Output
# Compile the model
model.compile(
  optimizer='adam',             # How model learns
  loss='binary_crossentropy',  # How model calculates error
  metrics=['accuracy']         # What performance you want to track
)

#  Train the model
history = model.fit(X_train, y_train, validation_split=0.2, epochs=100, verbose=0)

#  Plot Loss Graph
plt.plot(history.history['loss'], label='Training Loss')
plt.plot(history.history['val_loss'], label='Validation Loss')
plt.title('Loss Over Epochs')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()

#  Evaluate Model
loss, accuracy = model.evaluate(X_test, y_test)
print(f"\nTest Accuracy: {accuracy*100:.2f}%")

#  Predictions
y_pred = model.predict(X_test)
y_pred
y_pred_classes = (y_pred > 0.5).astype(int)
y_pred_classes

#  Confusion Matrix & Classification Report
cm = confusion_matrix(y_test, y_pred_classes)
print("\nConfusion Matrix:\n", cm)
cr = classification_report(y_test, y_pred_classes)
print("\nClassification Report:\n", cr)

#  Visualize Confusion Matrix
plt.figure(figsize=(5, 5))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.title('Confusion Matrix')
plt.show()

new_data = np.array([[48,1,1,90,123,1,0,162,0,1.1,2,1,1]])
new_data_scaled = scaler.transform(new_data)
prediction = model.predict(new_data_scaled)
result = (prediction > 0.5).astype(int)
print("Heart Disease Present?" , result)