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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| Vydáno v: | Diabetes, metabolic syndrome and obesity Ročník 14; s. 2789 - 2806 |
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Dove Medical Press Limited
01.01.2021
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| Abstract | 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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| AbstractList | Purpose: 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. Methods: 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. Results: 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%. Conclusion: The AFA-GBT model could classify patient diagnoses into the three diabetes types efficiently. Keywords: diabetes, GBT, feature selection, artificial flora, classification Purpose: 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. Methods: 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. Results: 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%. Conclusion: The AFA-GBT model could classify patient diagnoses into the three diabetes types efficiently. Nagaraj P,1 Deepalakshmi P,1 Romany F Mansour,2 Ahmed Almazroa3 1Department of Computer Science and Engineering, School of Computing, Kalasalingam Academy of Research and Education, Virudhunagar, Tamil Nadu, India; 2Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, Egypt; 3Department of imaging Research, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Science, Riyadh, Saudi ArabiaCorrespondence: Nagaraj PDepartment of Computer Science and Engineering, School of Computing, Kalasalingam Academy of Research and Education, Anand Nagar, Krishnankoil, Srivilliputtur, Virudhunagar, Tamil Nadu, 626126, IndiaEmail nagaraj.p@klu.ac.inPurpose: 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.Methods: 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.Results: 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%.Conclusion: The AFA-GBT model could classify patient diagnoses into the three diabetes types efficiently.Keywords: diabetes, GBT, feature selection, artificial flora, 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.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.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.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.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%.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%.The AFA-GBT model could classify patient diagnoses into the three diabetes types efficiently.CONCLUSIONThe AFA-GBT model could classify patient diagnoses into the three diabetes types efficiently. 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. |
| Audience | Academic |
| Author | Almazroa, Ahmed Mansour, Romany F P, Nagaraj P, Deepalakshmi |
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| Snippet | Classification of medical data is essential to determine diabetic treatment options; therefore, the objective of the study was to develop a model to classify... Purpose: Classification of medical data is essential to determine diabetic treatment options; therefore, the objective of the study was to develop a model to... Nagaraj P,1 Deepalakshmi P,1 Romany F Mansour,2 Ahmed Almazroa3 1Department of Computer Science and Engineering, School of Computing, Kalasalingam Academy of... |
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| SubjectTerms | 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 |
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| Title | Artificial Flora Algorithm-Based Feature Selection with Gradient Boosted Tree Model for Diabetes Classification |
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