Summary of Advancements in Heart Disease Prediction: a Machine Learning Approach For Early Detection and Risk Assessment, by Balaji Shesharao Ingole et al.
Advancements In Heart Disease Prediction: A Machine Learning Approach For Early Detection And Risk Assessment
by Balaji Shesharao Ingole, Vishnu Ramineni, Nikhil Bangad, Koushik Kumar Ganeeb, Priyankkumar Patel
First submitted to arxiv on: 16 Oct 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 This paper explores the application of machine learning models in predicting heart disease risks using clinical data. By analyzing cross-sectional clinical data, the study aims to comprehend the role of various features in classifying patients with and without heart disease. The research focuses on seven machine learning classifiers: Logistic Regression, Random Forest, Decision Tree, Naive Bayes, k-Nearest Neighbors, Neural Networks, and Support Vector Machine (SVM). The performance of each model is evaluated based on accuracy metrics, with the SVM demonstrating the highest accuracy at 91.51%. This study highlights the benefits of advanced computational methodologies in evaluating, predicting, improving, and managing cardiovascular risks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computer programs called machine learning models to help doctors predict if someone might get heart disease. They looked at lots of information about patients who already had or didn’t have heart disease. The researchers wanted to see which kinds of information were most important for predicting the risk of getting heart disease. They tested seven different ways of using this information, and one method called Support Vector Machine (SVM) was best at making accurate predictions. |
Keywords
» Artificial intelligence » Decision tree » Logistic regression » Machine learning » Naive bayes » Random forest » Support vector machine
