Artificial Flora Algorithm-Based Feature Selection with Gradient Boosted Tree Model for Diabetes Classification

Classification of medical data is essential to determine diabetic treatment options; therefore, the objective of the study was to develop a model to classify the three diabetes type diagnoses according to multiple patient attributes. Three different datasets are used to develop a novel medical data...

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Bibliographic Details
Published in:Diabetes, metabolic syndrome and obesity Vol. 14; pp. 2789 - 2806
Main Authors: P, Nagaraj, P, Deepalakshmi, Mansour, Romany F, Almazroa, Ahmed
Format: Journal Article
Language:English
Published: New Zealand Dove Medical Press Limited 01.01.2021
Taylor & Francis Ltd
Dove
Dove Medical Press
Subjects:
Accuracy
Algorithms
Analysis
artificial flora
Artificial intelligence
Classification
Comparative analysis
Data mining
Datasets
Decision trees
Diabetes
Diagnosis
Discriminant analysis
Electronic health records
Feature selection
gbt
Genetic algorithms
Gestational diabetes
Hypoglycemic agents
Medical advice systems
Medical records
Optimization
Original Research
Patients
Support vector machines
Type 2 diabetes
India
GBT
artificial flora
classification
diabetes
feature selection
ISSN:1178-7007, 1178-7007
Online Access:Get full text
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Summary:Classification of medical data is essential to determine diabetic treatment options; therefore, the objective of the study was to develop a model to classify the three diabetes type diagnoses according to multiple patient attributes. Three different datasets are used to develop a novel medical data classification model. The proposed model involved preprocessing, artificial flora algorithm (AFA)-based feature selection, and gradient boosted tree (GBT)-based classification. Then, the processing occurred in two steps, namely, format conversion and data transformation. AFA was applied for selecting features, such as demographics, vital signs, laboratory tests, medications, from the patients' electronic health records. Lastly, the GBT-based classification model was applied for classifying the patients' cases to type I, type II, or gestational diabetes mellitus. The effectiveness of the proposed AFA-GBT model was validated using three diabetes datasets to classify patient cases into one of the three different types of diabetes. The proposed model showed a maximum average precision of 91.64%, a recall of 97.46%, an accuracy of 99.93%, an F-score of 94.19%, and a kappa of 96.61%. The AFA-GBT model could classify patient diagnoses into the three diabetes types efficiently.
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ISSN:1178-7007
1178-7007
DOI:10.2147/DMSO.S312787