Heart Disease Prediction using Machine Learning
By Prof. M. M. Bhajibhakare. International Journal for Research in Applied Science and Engineering Technology, 2019
472 citations recorded by the source corpus.
-The large amount of data is generated in medical organizations (hospitals, medical centers), but this data is not properly used. There is a wealth of hidden information present in the datasets. This unused data can be converted into useful data by using different data mining techniques. This paper presents a classifier approach for detection of heart disease and shows how Naive Bayes can be used for classification purpose. This system will categorize medical data into five categories namely no, low, average, high and very high. If unknown sample comes then the system will predict the class label of that sample. Hence two basic functions namely classification (training) and prediction (testing) will be performed
Subfields: Health Information Management
View Paper: https://www.semanticscholar.org/paper/205bebb4a332a54839c3f3866d15f6e551320914
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Health Information Management
- Amna Eleyan
- Parth Patel
- Chintan M. Bhatt
- Dina Jrab
- Tarek Bejaoui
- Derar Eleyan
- Pier Luigi Mazzeo
- Ta Ghetia
Citations in the corpus
- Heart Disease Prediction Using Machine Learning Algorithms (2024, methodology): “Various metrics have been employed to assess the performance of classifiers [31], [32], [48].”
- Effective Heart Disease Prediction Using Machine Learning Techniques (2023, methodology): “The samples produced by the decision tree may be saved and used with various data types [31].”