Project conducted by R.P.M. Kras, Vrije Universiteit Amsterdam 2023

Bachelor of Science, Computer Science graduation project

Model 1, Random forest

Imported libraries

import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from imblearn.over_sampling import RandomOverSampler
import seaborn as sns
import matplotlib.pyplot as plt

Load data and define feature columns and target variable

# Read data
data = pd.read_csv("datasets/heartdisease.csv")

# Define your feature columns and target variable
X = data.drop('HeartDisease', axis=1)
y = data['HeartDisease']

Feature engineering: encode categorical data

# Feature engineering of the categorical data
label_encoders = {}
for col in X.columns:
    le = LabelEncoder()
    X.loc[:, col] = le.fit_transform(X[col])
    label_encoders[col] = le

Train test split and oversampling of minority classes

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1, stratify=y)

# Oversample the minority class to balance the dataset
oversampler = RandomOverSampler(random_state=1)
X_train_balanced, y_train_balanced = oversampler.fit_resample(X_train, y_train)

Training of the model on the balanced data

# Train the model on the balanced data
rf_model = RandomForestClassifier(random_state=1)  # Set a seed for reproducibility
rf_model.fit(X_train_balanced, y_train_balanced)

# Make predictions
y_pred = rf_model.predict(X_test)

Results

# Make predictions
y_pred = rf_model.predict(X_test)

# Generate and display confusion matrix
class_labels = sorted(y.unique())  # Get unique class labels

# Create confusion matrix
conf_matrix = confusion_matrix(y_test, y_pred, labels=class_labels)

# Display confusion matrix using a heatmap
plt.figure(figsize=(8, 6))
sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=class_labels, yticklabels=class_labels)
plt.xlabel('Predicted Labels')
plt.ylabel('Actual Labels')
plt.title('Confusion Matrix - Testing Results')
plt.show()