Improved diabetes disease prediction IWFO model using machine learning algorithms

Title

Improved diabetes disease prediction IWFO model using machine learning algorithms

Creator

Gunavathi R.; Sivakumar V.; Kumar B.S.; Vijayalakshmi S.

Description

Diabetic disease is the mostly affected and massive disease on a global level. Diagnosing the diabetic earlier will help the medicalist to give the improved and latest clinical treatment. The healthcare specialist unit uses many machine learning techniques, methodologies and tools for decision making in diabetic field. The machine learning techniques are utilized for the prediction of the diabetic diseases in the initial level. To eliminate such issues, optimized detection techniques are proposed. First of all, the training samples are increased using the sliding window protocol. Further, class imbalanced training data classes are balanced and resolved using the adaptive and gradient booster technique. Further, the diabetic feature selection process is improved by the Intensity Weighted Firefly Optimization firefly techniques (IWFO), in which irrelevant features are reduced based on the correlation between the features that deducts the unwanted features involved in the diabetic disease process. Then the feature transformation problem is faced by the PCA technique, which manages the several types of features. Finally, the improved and optimal hybrid random forest is applied into the normal and diabetes classes respectively. The proposed system predicts the diabetic disease efficiently and maximizes its precision of the prediction system. The present paper is compared with different classifiers to determine the efficiency of the work. Overall, the initiated system improved the present studies accuracy level. 2024 Author(s).

Source

AIP Conference Proceedings, Vol-3161, No. 1

Date

2024-01-01

Publisher

American Institute of Physics

Coverage

Gunavathi R., School of Science, Department of Data Science, Christ University, Pune, India; Sivakumar V., School of Computing, Asia Pacific University of Technology and Innovation, Kuala Lumpur, Malaysia; Kumar B.S., Department of Computer Science, Sree Narayana Guru College, Coimbatore, India; Vijayalakshmi S., School of Science, Department of Data Science, Christ University, Pune, India

Rights

Restricted Access

Relation

ISSN: 0094243X

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1063/5.0229494

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203957539&doi=10.1063%2f5.0229494&partnerID=40&md5=72023ea6c153c360ab409219e1ca248a