Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases
Adedayo Ogunpola1, Faisal Saeed1, Shadi Basurra1
1DAAI Research Group, College of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK.
Insights
This study enhances early heart disease detection using machine learning, particularly XGBoost, achieving 98.50% accuracy. It addresses imbalanced datasets for more reliable cardiovascular disease prediction.
Area of Science:
- Cardiology
- Computer Science
- Data Science
Background:
- Cardiovascular diseases pose a significant global health challenge.
- Existing detection methods require advancement, particularly in handling imbalanced datasets which can bias predictions.
- Early detection of myocardial infarction is crucial for improving patient outcomes.
Purpose of the Study:
- To develop accurate and effective early detection methods for heart diseases, focusing on myocardial infarction.
- To address the challenge of imbalanced datasets in cardiovascular disease prediction models.
- To evaluate the performance of various machine learning and deep learning classifiers for heart disease detection.
Main Methods:
- A comprehensive literature review was conducted to identify strategies for handling imbalanced datasets.
- Seven machine learning and deep learning classifiers were deployed: K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Convolutional Neural Network, Gradient Boost, XGBoost, and Random Forest.
- The XGBoost model was meticulously fine-tuned for cardiovascular disease prediction.
Main Results:
- The fine-tuned XGBoost model achieved high performance metrics: 98.50% accuracy, 99.14% precision, 98.29% recall, and 98.71% F1 score.
- The study demonstrated the effectiveness of XGBoost in enhancing diagnostic accuracy for heart disease.
- Performance insights were gained across multiple classifiers for robust prediction model development.
Conclusions:
- Optimized machine learning models, particularly XGBoost, significantly improve the accuracy of early heart disease detection.
- Addressing imbalanced datasets is critical for developing unbiased and reliable cardiovascular disease prediction tools.
- This research provides a strong foundation for developing advanced diagnostic systems for myocardial infarction.
Abstract:
Cardiovascular diseases present a significant global health challenge that emphasizes the critical need for developing accurate and more effective detection methods. Several studies have contributed valuable insights in this field, but it is still necessary to advance the predictive models and address the gaps in the existing detection approaches. For instance, some of the previous studies have not considered the challenge of imbalanced datasets, which can lead to biased predictions, especially when the datasets include minority classes. This study's primary focus is the early detection of heart diseases, particularly myocardial infarction, using machine learning techniques. It tackles the challenge of imbalanced datasets by conducting a comprehensive literature review to identify effective strategies. Seven machine learning and deep learning classifiers, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Convolutional Neural Network, Gradient Boost, XGBoost, and Random Forest, were deployed to enhance the accuracy of heart disease predictions. The research explores different classifiers and their performance, providing valuable insights for developing robust prediction models for myocardial infarction. The study's outcomes emphasize the effectiveness of meticulously fine-tuning an XGBoost model for cardiovascular diseases. This optimization yields remarkable results: 98.50% accuracy, 99.14% precision, 98.29% recall, and a 98.71% F1 score. Such optimization significantly enhances the model's diagnostic accuracy for heart disease.
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