Summary of Classification and Prediction Of Heart Diseases Using Machine Learning Algorithms, by Akua Sekyiwaa Osei-nkwantabisa et al.
Classification and Prediction of Heart Diseases using Machine Learning Algorithms
by Akua Sekyiwaa Osei-Nkwantabisa, Redeemer Ntumy
First submitted to arxiv on: 5 Sep 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 research aims to identify the most effective machine learning algorithm for predicting heart diseases, which is a leading cause of death worldwide. The study compares four approaches: Logistic Regression, K-Nearest Neighbor, Support Vector Machine, and Artificial Neural Networks. The UCI heart disease repository provides the dataset, and the results show that the K-Nearest Neighbor technique outperforms others in determining patient risk. The findings suggest that machine learning can be a valuable tool for improving heart disease prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Heart disease is a major global health issue because it claims many lives. Scientists want to create better tools to predict when someone might develop this condition. Current methods are either expensive or hard to use, so the goal of this study was to find the best machine learning method for predicting heart diseases. The researchers compared four different approaches and found that one called K-Nearest Neighbor worked the best. |
Keywords
» Artificial intelligence » Logistic regression » Machine learning » Nearest neighbor » Support vector machine
