Summary of Comparative Study Of Machine Learning Algorithms in Detecting Cardiovascular Diseases, by Dayana K et al.
Comparative Study of Machine Learning Algorithms in Detecting Cardiovascular Diseases
by Dayana K, S. Nandini, Sanjjushri Varshini R
First submitted to arxiv on: 27 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 This study compares various machine learning algorithms to detect cardiovascular diseases (CVD) more accurately and efficiently. It evaluates logistic regression, decision tree, random forest, gradient boosting, support vector machine (SVM), K-nearest neighbors (KNN), and XGBoost models on a structured workflow that includes data collection, preprocessing, model selection, hyperparameter tuning, training, evaluation, and choosing the optimal model. The results show that ensemble methods and advanced algorithms provide reliable predictions, offering a comprehensive framework for CVD detection that can be implemented in clinical settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cardiovascular diseases (CVD) are a major health concern. This study uses machine learning to develop new ways to detect these diseases early. It compares different types of machine learning models to see which ones work best. The goal is to make diagnoses more accurate and efficient. The researchers used a step-by-step process to collect and prepare data, choose the right model, and fine-tune its performance. They found that combining different models can be very effective in detecting CVD. This research can help doctors and hospitals develop better diagnostic tools. |
Keywords
» Artificial intelligence » Boosting » Decision tree » Hyperparameter » Logistic regression » Machine learning » Random forest » Support vector machine » Xgboost
