Heart Disease Prediction Using Logistic Regression

Cardiovascular diseases (CVDs) are one of the leading causes of death worldwide, and early identification is crucial to improve the patient’s prognosis. Traditional diagnostic methods for heart diseases are highly dependent on clinical expertise and can be time-consuming, making machine learning an attractive alternative for improving accuracy to diagnose and support faster decision making. Previously, research has explored various machine learning algorithms, such as decision trees, support vector machines, and neural networks, to improve prediction accuracy. However, many of these models face challenges related to their interpretability and generalization to unexpected data. This paper presents a logistic regression model to predict CVDs based on a dataset of 303 samples, incorporating key demographic, clinical, and diagnostic features. The model achieved an accuracy of $85.12 \%$ in the training data and 81. $97 \%$ in the testing data, demonstrating its effectiveness for binary classification tasks. The study emphasizes the computational efficiency and interpretability of logistic regression, making it appropriate for real-time applications in clinical settings. In conclusion, the results indicate that logistic regression can serve as a reliable tool for early detection of heart disease, and future work can explore dataset expansion, advanced models, and explainable AI techniques to further improve prediction accuracy and model transparency.