Summary of The Impact Of Ontology on the Prediction Of Cardiovascular Disease Compared to Machine Learning Algorithms, by Hakim El Massari et al.
The Impact of Ontology on the Prediction of Cardiovascular Disease Compared to Machine Learning Algorithms
by Hakim El Massari, Noreddine Gherabi, Sajida Mhammedi, Hamza Ghandi, Mohamed Bahaj, Muhammad Raza Naqvi
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| 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 paper compares and reviews various machine learning algorithms, including ontology-based Machine Learning classification, to identify heart illness and diagnose cardiovascular disease early. It explores seven classification methods: Random Forest, Logistic regression, Decision Tree, Naive Bayes, k-Nearest Neighbours, Artificial Neural Network, and Support Vector Machine. The dataset consists of 70,000 instances and can be downloaded from Kaggle. The findings are assessed using performance metrics such as F-Measure, Accuracy, Recall, and Precision. The results show that the ontology outperforms all machine learning algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper compares different ways to use machine learning to diagnose heart problems. It looks at several methods like Random Forest, Logistic regression, and others to see which one works best. They used a big dataset with 70,000 examples to test their ideas. The results showed that using an ontology (a way of organizing information) was the best approach. |
Keywords
» Artificial intelligence » Classification » Decision tree » Logistic regression » Machine learning » Naive bayes » Neural network » Precision » Random forest » Recall » Support vector machine
