Comparative Analysis of Machine Learning Techniques for Enhanced Predictive Modeling in Healthcare
Publication Date
1-1-2024
Document Type
Conference Proceeding
Publication Title
International IEEE Conference proceedings, IS
Issue
2024
DOI
10.1109/IS61756.2024.10705168
Abstract
Recently, there has been a surge in predictive modeling in healthcare because it can save lives through early diagnostics and treatments. Advances in algorithms and computing power can be harnessed to develop predictive models using Python-based programs and automated ML platforms. This study compares the performance of neural networks and automated ML models in predicting coronary heart disease using data from the UCI repository. We utilized Python-based neural networks and IBM Watson's Snap Logistic Regression, Extra Trees Classifier, and Logistic Regression. Our results show that IBM Watson's models achieved an accuracy of 86%, outperforming the neural network's 80%. This comparative analysis highlights the potential of automated ML platforms in enhancing predictive modeling in healthcare.
Keywords
Artificial Intelligence, Coronary Heart Disease, Machine Learning, Neural Networks
Department
Mechanical Engineering
Recommended Citation
Advika Arya, Farwa Kazmi, and Sohail Zaidi. "Comparative Analysis of Machine Learning Techniques for Enhanced Predictive Modeling in Healthcare" International IEEE Conference proceedings, IS (2024). https://doi.org/10.1109/IS61756.2024.10705168
