heart failure prediction dataset (Kaggle Inc)
86
Structured Review
Kaggle Inc
heart failure prediction dataset
Heart Failure Prediction Dataset, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/heart+failure+dataset/dataset+disease+heart/pm42120489-248-3-27
Average 86 stars, based on 1 article reviews
Heart Failure Prediction Dataset, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/heart+failure+dataset/dataset+disease+heart/pm42120489-248-3-27
Average 86 stars, based on 1 article reviews
heart failure prediction dataset - by Bioz Stars,
2026-09
86/100 stars
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Related Articles
other:Article Title: Prediction and Feature Importance Analysis for Heart Failure using Machine Learning Techniques Article Snippet: This paper uses a Article Title: Clinical Support System for Cardiovascular Disease Forecasting Using ECG Article Snippet: The Article Title: Machine Learning Techniques for Heart Disease Prediction Using a Multi-Algorithm Approach Article Snippet: The research carried out begins with data preparation, then continues with data preprocessing, splitting the data, testing the machine learning model, getting test results in the form of matrix evaluations, and the final step is comparing the evaluations. Article Title: Enhancing heart disease classification based on greylag goose optimization algorithm and long short-term memory. Article Snippet: The study utilizes the Article Title: Heart Failure Prediction Using Variational Auto-Encoder and Extreme Gradient Boosted Neural Network Article Snippet: This paper pioneers the integration of Variational Auto-encoder (VAE) techniques into the XGBNet model for heart failure prediction, leveraging the largest combined heart failure dataset from Kaggle.. Through rigorous evaluation over 100 epochs, the model achieved a 92% prediction accuracy in distinguishing patients with potential heart failure.. Comparative analysis revealed a 2-3% increase in accuracy over previous methodologies, highlighting the efficacy of VAE in tandem with XGBoost. Article Title: Prediction and Feature Importance Analysis for Heart Failure using Machine Learning Techniques Article Snippet: A Article Title: Predicting and Analyzing Cardiovascular Disease through Ensemble Learning Approaches Article Snippet: This study makes use of the heart failure dataset from the Article Title: The Application of Support Vector Machines and Artificial Neural Networks to the Prediction of Heart Disease Article Snippet: Nanotechnology Perceptions ISSN 1660-6795 www.nano-ntp.com Nanotechnology Perceptions 20 No. S14 (2024) 2514–2526 |