Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
Abstract
Datasets are gathered for different diseases and then the feature is extracted using dimensionality reduction techniques. After the attributes are reduced, the attributes are used to test and train the data using decision tree classification algorithm techniques. The various decision tree algorithms are also used to find the accuracy for each diseases and then hybridization techniques are used to solve the problem which is then used to create upgraded yield. Metaheuristic is generally a search algorithm which solves the optimization problems that provides the best solution from the available solutions. It provides a better solution with less effort compared with other algorithms. One of the recent trend is hybrid optimization methods. Hybridization of metaheuristics are nothing but combining two bio-inspired algorithms. It improves algorithmic performance in a more efficient way to solve the problems. Hybridization techniques extracts the strengths from the combination of each algorithm.
How to cite this article:
J. Gitanjali and R. Subhashini. Enhanced Bio-Inspired Algorithm for Disease Diagnosis.
DOI: https://doi.org/10.36478/jeasci.2020.3024.3031
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2020.3024.3031