Summary of Centralized and Federated Heart Disease Classification Models Using Uci Dataset and Their Shapley-value Based Interpretability, by Mario Padilla Rodriguez et al.
Centralized and Federated Heart Disease Classification Models Using UCI Dataset and their Shapley-value Based Interpretability
by Mario Padilla Rodriguez, Mohamed Nafea
First submitted to arxiv on: 12 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study benchmarks centralized and federated machine learning algorithms for heart disease classification using the UCI dataset, which includes 920 patient records from four hospitals in the USA, Hungary, and Switzerland. The benchmark is supported by Shapley-value interpretability analysis to quantify features’ importance for classification. In the centralized setup, various binary classification algorithms are trained on pooled data, with a support vector machine (SVM) achieving the highest testing accuracy of 83.3%, surpassing the established benchmark of 78.7% with logistic regression. Federated learning algorithms with four clients (hospitals) are explored, leveraging the dataset’s natural partition to enhance privacy without sacrificing accuracy. Federated SVM achieves a top testing accuracy of 73.8%. The interpretability analysis aligns with existing medical knowledge of heart disease indicators. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding better ways to diagnose heart diseases using machine learning. It compares different methods for classifying patients with heart disease, and finds that one method called support vector machine (SVM) works best. This method was able to correctly identify 83% of patients with heart disease. The study also looked at a new way of doing this, called federated learning, which lets different hospitals share their data without sharing individual patient information. This approach worked almost as well as the standard SVM method. |
Keywords
» Artificial intelligence » Classification » Federated learning » Logistic regression » Machine learning » Support vector machine
