Summary of Multi-class Heart Disease Detection, Classification, and Prediction Using Machine Learning Models, by Mahfuzul Haque et al.
Multi-class heart disease Detection, Classification, and Prediction using Machine Learning Models
by Mahfuzul Haque, Abu Saleh Musa Miah, Debashish Gupta, Md. Maruf Al Hossain Prince, Tanzina Alam, Nusrat Sharmin, Mohammed Sowket Ali, Jungpil Shin
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 introduces a novel approach to heart disease detection (HDD) in Bangladesh by developing a new dataset, BIG-Dataset, and CD dataset. The proposed system uses machine learning techniques like Logistic Regression and Random Forest to achieve an impressive testing accuracy of up to 96.6%. By integrating these models and datasets, the AI-driven system provides real-time diagnostics and personalized healthcare recommendations. This innovative solution has the potential to reduce mortality rates and improve clinical outcomes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to detect heart disease in Bangladesh using special computer programs. Researchers created two big sets of data (BIG-Dataset and CD dataset) that includes information on symptoms, medical tests, and risk factors. They used these datasets with machine learning techniques like Logistic Regression and Random Forest to make accurate predictions. The goal is to create a system that can quickly diagnose heart disease and give people personalized advice for taking care of their health. |
Keywords
» Artificial intelligence » Logistic regression » Machine learning » Random forest
