Abstract: Hepatitis is one of the most common diseases in the world and any early diagnosis can save the lives of many people suffering from this disease. The purpose of this research is to diagnose hepatitis disease using the combined model of the decision tree algorithm and Harris Hawks Optimization.
In this research, the diagnosis of hepatitis disease was made using the decision tree and evolutionary algorithm of Harris Hawks Optimization . HHO algorithm is a population-based and gradient-independent optimization technique. The main idea of the HHO algorithm is the cooperative behavior and chasing style of Harris's falcon in nature, which is known as surprise attack. The effectiveness of the proposed HHO optimizer method, compared to other nature-inspired techniques, was tested on 29 functions and several real-world engineering problems were investigated. The statistical results and comparisons show that the HHO algorithm has very promising and sometimes competitive results compared to other well-known meta-heuristic techniques [6].
Keywords: Decision Tree, Evolutionary Algorithms, Data Mining, Harris Hawks Optimization (HHO)
Works Cited:
Mohammad Ordouei, Mastooreh oeini "Identification of hepatitis disease by combining decision tree algorithm and Harris Hawks Optimization (HHO)", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 7, pp. 1-6, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12701
|
DOI:
10.17148/IJARCCE.2023.12701
