ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2021
Artificial Flora Algorithm-Based Feature Selection with Gradient Boosted Tree Model for Diabetes Classification.
Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Predictors for the Longevity of People with Diabetes in Buno Bedele and Illubabor Zones, South-west Ethiopia.Diabetes, metabolic syndrome and obesity : targets and therapy · 2023Article
- Semi-Supervised Instance-Segmentation Model for Feature Transfer Based on Category Attention.Sensors (Basel, Switzerland) · 2022Article
- Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2022Article
- A machine learning-based clinical decision support system for effective stratification of gestational diabetes mellitus and management through Ayurveda.Journal of Ayurveda and integrative medicineArticle
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Authors and funding
4 authors.
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Abstract
purposeClassification 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.
methodsThree 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.
resultsThe 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%.
conclusionThe AFA-GBT model could classify patient diagnoses into the three diabetes types efficiently.
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