Cardiovascular diseases prediction by machine learning incorporation with deep learning
Sivakannan Subramani1, Neeraj Varshney2, M Vijay Anand3
1Department of Advanced Computing, St. Joseph's University, Bengaluru, Karnataka, India.
Frontiers in Medicine
|May 4, 2023
Summary
This study introduces advanced machine learning models for predicting cardiovascular disease (CVD) outcomes using Internet of Things (IoT) data. The new approach achieves nearly 96% accuracy, improving upon existing methods for better patient care.
Area of Science:
- Artificial Intelligence in Medicine
- Machine Learning for Healthcare
- Cardiovascular Disease Research
Background:
- Cardiovascular disease (CVD) poses a significant risk of mortality, morbidity, and disability, with its exact causes remaining unclear.
- There is a critical need for AI-driven technologies capable of accurate and timely prediction of individual patient outcomes in CVD.
- The Internet of Things (IoT) is increasingly integral to collecting health data, necessitating advanced analytical methods.
Purpose of the Study:
- To develop and evaluate novel machine learning (ML) models for improved prediction of cardiovascular disease outcomes.
- To address the limitations of traditional ML algorithms in handling data variability and prediction accuracy.
- To leverage IoT data for enhanced CVD risk assessment and patient management.
Main Methods:
- Development of a suite of ML models incorporating diverse data observation mechanisms and training procedures.
- Integration of the Heart Dataset with various classification models to assess predictive performance.
- Comparative analysis of the proposed models against existing methods using multiple performance metrics.
Main Results:
- The proposed ML models demonstrated a significant improvement in prediction accuracy, achieving nearly 96%.
- The study provided a comprehensive analysis of model performance across several key metrics.
- The developed approach offers a more accurate and reliable method for CVD outcome prediction compared to traditional techniques.
Conclusions:
- The novel ML models show high efficacy in predicting cardiovascular disease outcomes, offering a substantial advancement over existing methods.
- The findings highlight the potential of IoT data and advanced ML for proactive cardiovascular healthcare.
- Further research involving large-scale medical datasets is recommended for developing sophisticated artificial neural network structures.
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