Performance Evaluation of Various Approaches for
Disease Prediction using Weka
Journal:
GRENZE International Journal of Engineering and Technology
Authors:
Sushil Kalmegh, Prerna S. Tayade, Amol P. Bhagat
Volume:
10
Issue:
2
Grenze ID:
01.GIJET.10.2.501_1
Pages:
425-434
Abstract
This paper introduces Weka, which uses data mining algorithms to classify viruses.
This paper introduces Weka’s proposed data mining algorithm for classifying viruses. This
paper presents a review and evaluation of a data mining algorithm for classifying diseases using
Weka. The data mining algorithms ZeroR, Linear Regression, Multilayer Perceptron, Random
Forest, Simple K-Means, Hierarchical Clustering, and Farthest First are proposed for disease
classification using Weka. Weka acts as the judge in evaluating these algorithms, which possess
the capability to accurately classify any ailment. Their performance is scrutinized based on
both precision and the time taken to classify diseases.