Detection of cardiovascular disease using KNN in comparison with naive bayes to measure precision, recall and f-score

The goal of this study is to find ways to use machine learning classifiers to spot heart disease. In this research, the k-nearest neighbors’ classifier is used to find signs of cardiovascular disease, and its performance is compared to that of the Naive Bayes classifier. A total of 70,000 samples in...

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Bibliographic Details
Main Authors: Sravani, S., Karthikeyan, P. R.
Format: Conference Proceeding
Language:English
Subjects:
Classifiers
Heart diseases
K-nearest neighbors algorithm
Machine learning
Recall
Online Access:Get full text
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Summary:The goal of this study is to find ways to use machine learning classifiers to spot heart disease. In this research, the k-nearest neighbors’ classifier is used to find signs of cardiovascular disease, and its performance is compared to that of the Naive Bayes classifier. A total of 70,000 samples in the cardiovascular dataset from the Kaggle repository is downloaded. This collection is divided into training samples ((n = 56000 (80%)) and test samples ((n = 14000 (20 %)) by fixing the g power value at 0.8. KNN achieved a precision, recall and f-score of 94.2%, 94.6%, and 95.0% respectively when compared to 86.8%, 84.8%, and 87.2% obtained for Naive Bayes algorithm (p
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0177014