Implementation of Machine Learning Algorithms for Cardiovascular Disease Prediction

Cardiovascular diseases are the leading cause of death every year in the world. By utilizing machine learning algorithms, patterns and relationships in data can be learned and utilized to make predictions and uncover hidden insights not immediately noticeable to humans. The effectiveness of different algorithms is measured and compared based on accuracy, precision, and recall. This paper investigates the impact of different feature selection strategies on the performance of these algorithms by comparing the performance of diverse algorithms such as decision trees, random forests, SVMs, neural networks, and logistic regression on a dataset of patients with proven CVD outcomes. The outcome reveals that machine learning can be an effective tool for predicting cardiovascular disease and identifying high-risk patients for early intervention and prevention. The findings have suggested the potential to improve the diagnosis and treatment of cardiovascular disease, enabling healthcare providers to intervene earlier and potentially prevent adverse outcomes. Further, we investigated how different hyperparameters and feature selection techniques affect the effectiveness of these algorithms. The study anticipated shed light on the most effective and understandable ML-based strategies for CVD prediction and treatment and will help them become more widely used in clinical settings. Thus, it can be stated that machine learning can be an effective tool for predicting cardiovascular disease and identifying high-risk patients for early intervention and prevention. These findings can have the potential to improve diagnosis and treatment of cardiovascular disease and could ultimately improve patient outcomes.