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About Using Machine Learning For Heart Disease Prediction: February 2021
The document discusses using machine learning techniques to predict heart disease. It begins with an introduction on the prevalence and risk factors of heart disease. It then discusses previous related work on feature selection and machine learning algorithms for heart disease prediction. For feature selection, previous studies identified the most commonly used attributes. For algorithms, neural networks, SVM, logistic regression, KNN, naive bayes and decision trees have been applied with accuracies ranging from 82-99%. The authors then describe applying neural networks, KNN and SVM on their dataset of Algerian patients to predict heart disease.
Detailed Information
| Author: | ['fgfgfgf'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 15 |
| Language: | English |
| Format: | |
| Price: | FREE |
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