Performance Evaluation of Various Approaches for Disease Prediction using Weka.

Autor:	Kalmegh, Sushil, Tayade, Prerna S., Bhagat, Amol P.
Předmět:	DATA mining RANDOM forest algorithms NOSOLOGY HIERARCHICAL clustering (Cluster analysis) FORECASTING MULTILAYER perceptrons
Zdroj:	Grenze International Journal of Engineering & Technology (GIJET); Jun2024, Vol. 10 Issue 2, Part 2, p425-434, 10p
Abstrakt:	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. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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