ItemArticle

Metaheuristic Machine Learning Algorithms for Liver Disease Prediction

Title
Metaheuristic Machine Learning Algorithms for Liver Disease Prediction
Creator
Gupta D.; Rani B.K.; Verma I.; Ahamed S.K.; Arul Mary Rexy V.; Rajkumar N.; Vidhya R.G.
Description
In machine learning, optimizing solutions is critical for improving performance. This study explores the use of metaheuristic algorithms to enhance key processes such as hyperparameter tuning, feature selection, and model optimization. Specifically, we integrate the Artificial Bee Colony (ABC) algorithm with Random Forest and Decision Tree models to improve the accuracy and efficiency of disease prediction. Machine learning has the potential to uncover complex patterns in medical data, offering transformative capabilities in disease diagnosis. However, selecting the optimal algorithm for model optimization presents a significant challenge. In this work, we employ Random Forest, Decision Tree models, and the ABC algorithmbased on the foraging behaviours of honeybeesto predict liver disease using a dataset from Indian medical records. Our experiments demonstrate that the Random Forest model achieves an accuracy of 85.12%, the Decision Tree model 76.89%, and the ABC algorithm 80.45%. These findings underscore the promise of metaheuristic approaches in machine learning, with the ABC algorithm proving to be a valuable tool in improving predictive accuracy. In conclusion, the integration of machine learning models with metaheuristic techniques, such as the ABC algorithm, represents a significant advancement in disease prediction, driving progress in data-driven healthcare. 2024, Iquz Galaxy Publisher. All rights reserved.
Source
International Research Journal of Multidisciplinary Scope, Vol-5, No. 4, pp. 651-660.
Date
2024-01-01
Publisher
Iquz Galaxy Publisher
Subject
Artificial Bee Colony (ABC) Algorithm; Decision Tree; Machine Learning; Random Forest
Coverage
Gupta D., Department of CSE, Manipal Institute of Technology Bengaluru, Bengaluru, India; Rani B.K., Department of Information Technology, Vasavi College of Engineering, Hyderabad, India; Verma I., School of Sciences, Christ (deemed to be) University, Delhi-NCR, Ghaziabad, India; Ahamed S.K., Department of CSE, Methodist College of Engineering and Technology, Hyderabad, India; Arul Mary Rexy V., Department of Commerce General, Saveetha College of Liberal Arts and Sciences, Chennai, India; Rajkumar N., Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology, Avadi, Tamil Nadu, India; Vidhya R.G., Department of ECE, HKBK College of Engineering, Bangalore, India
Rights
Restricted Access
Relation
ISSN: 2582631X
Format
Online
Language
English
Type
Article

Collection

Articles

Citation

Gupta D.; Rani B.K.; Verma I.; Ahamed S.K.; Arul Mary Rexy V.; Rajkumar N.; Vidhya R.G., “Metaheuristic Machine Learning Algorithms for Liver Disease Prediction,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/12782.

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