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

Bachelor of Science, Computer Science graduation project

Model 2, Neural network

Imported libraries

import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns

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 fitting logistic regression model to the data

Train model

# 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, shuffle=False)

# Fit logistic regression model
lr_model = LogisticRegression()
lr_model.fit(X_train, y_train)

# Make predictions
y_pred = lr_model.predict(X_test)

C:\Users\robkr\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

Results

# Generate confusion matrix
class_labels = le.classes_

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