Abstract:
The growing number of cardiovascular diseases is a big concern for health worldwide,
showing the need for effective tools to help detect these diseases early and provide
timely treatment. This research contributes to the growing body of knowledge in
healthcare analytics by investigating the effectiveness of machine learning algorithms
to enhance patient diagnosis and treatment strategies in cardiovascular care. We
compare four different Machin Learning Algorithms as K-Nearest Neighbors (K-NN),
Logistic Regression (LR), Support Vector Machine (SVM), and Naive Bayes (NB)
using a comprehensive dataset of patient attributes. Our methodology includes
preprocessing techniques such as one-hot encoding and label encoding to convert
categorical variables for optimal model performance. Additionally in our study, we
explored the influence of these preprocessing techniques, identifying that one-hot
encoding generally enhanced accuracy for most algorithms. Hyperparameter tuning was
conducted for SVM, optimizing parameters as the kernel type and regularization
strength, which further improved the model’s accuracy. The dataset was systematically
split into 80% training and 20% testing subsets, allowing us to assess each algorithm's
accuracy on the testing set. The results revealed that SVM outperformed the other
algorithms, achieving an accuracy of 89.69%, highlighting the critical role of
methodological choices in developing effective predictive models. What sets this
research apart from recent studies is its comprehensive comparison of multiple
algorithms alongside detailed data preprocessing techniques, providing insights into the
impact of these choices on predictive performance.
