Volume 20 No 22 (2022)
 Download PDF
Rotation Forest Algorithm With The Help Of Artificial Bee Colony For Medical Data Classification
Balasaheb Tarle and Vaishali Tidake
In recent years, medical data classification has become increasingly important in healthcare. Accurately classifying medical data has assisted in medical decision-making, leading to better patient outcomes. In this paper, we propose an approach for medical data classification using the Rotation Forest (RF) Algorithm with the help of Artificial Bee Colony (ABC) optimization The RF algorithm is an ensemble learning technique that combines multiple decision trees to create a more accurate prediction model, while the ABC algorithm is a metaheuristic optimization algorithm inspired by the foraging behavior of honeybees. We have compared the performance of our proposed approach with other popular classification methods such as AdaBoost, Support Vector Machines, and Random Forests. We observed that the ABC algorithm effectively optimized the parameters of the RF algorithm, leading to a more accurate classification model. The accuracy of our proposed approach was 98.75%. Our approach has been applied in various healthcare applications, such as disease diagnosis and risk prediction.
Artificial Bee Colony; Rotation Forest; Medical Data; Training Data; Feature Selection; Machine Learning.
Copyright © Neuroquantology

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Articles published in the Neuroquantology are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant IJECSE right of first publication under CC BY-NC-ND 4.0. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of articles in this journal, and to use them for any other lawful purpose.