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Heart Disease Classification Accuracy Analysis by aditya b is a document available to read on EtoBox.
What is Heart Disease Classification Accuracy Analysis about?
The document discusses the application of classification algorithms, specifically k nearest neighbour and random forest, on the Cleveland heart disease database, achieving accuracies of 83.16% and 91.6%, respectively. It highlights the historical context of classification accuracies in heart disease research, noting a previous high of 82%. Additionally, it covers related topics such as machine learning methods, data pre-processing, evaluation metrics, and the dataset description used in the study.
- Author
- aditya b
- Language
- EN