Design of intelligent diabetes mellitus detection system using hybrid feature selection based XGBoost classifier

Prabha, Anju; Yadav, Jyoti; Rani, Asha; Singh, Vijander

doi:10.1016/j.compbiomed.2021.104664

Cited by 70 publications

(25 citation statements)

References 45 publications

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“…The microaverage and macroaverage are also computed by summing the individual values for true positive (TP), true negative (TN), false positive (FP), and false negative (FN). Then, the accuracy (Equation ( 21 )), recall (Equation ( 22 )), precision (Equation ( 23 )), and F1-score (Equation ( 24 )) are selected as the important metrics to evaluate the performance of different classifiers [ 29 ].

…”

Section: Experiments and Resultsmentioning

confidence: 99%

Improved Multiclassification of Schizophrenia Based on Xgboost and Information Fusion for Small Datasets

Zhu

Shen

Zhang

2022

Computational and Mathematical Methods in Medicine

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To improve the performance in multiclass classification for small datasets, a new approach for schizophrenic classification is proposed in the present study. Firstly, the Xgboost classifier is introduced to discriminate the two subtypes of schizophrenia from health controls by analyzing the functional magnetic resonance imaging (fMRI) data, while the gray matter volume (GMV) and amplitude of low-frequency fluctuations (ALFF) are extracted as the features of classifiers. Then, the D-S combination rule of evidence is used to achieve fusion to determine the basic probability assignment based on the output of different classifiers. Finally, the algorithm is applied to classify 38 healthy controls, 16 deficit schizophrenic patients, and 31 nondeficit schizophrenic patients. 10-folds cross-validation method is used to assess classification performance. The results show the proposed algorithm with a sensitivity of 73.89%, which is higher than other classification algorithms, such as supported vector machine (SVM), logistic regression (LR), K -nearest neighbor (KNN) algorithm, random forest (RF), BP neural network (NN), classification and regression tree (CART), naive Bayes classifier (NB), extreme gradient boosting (Xgboost), and deep neural network (DNN). The accuracy of the fusion algorithm is higher than that of classifier based on the GMV or ALFF in the small datasets. The accuracy rate of the improved multiclassification method based on Xgboost and fusion algorithm is higher than that of other machine learning methods, which can further assist the diagnosis of clinical schizophrenia.

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…”

Section: Experiments and Resultsmentioning

confidence: 99%

Improved Multiclassification of Schizophrenia Based on Xgboost and Information Fusion for Small Datasets

Zhu

Shen

Zhang

2022

Computational and Mathematical Methods in Medicine

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show abstract

“…Compared to other models, the XGBoost model performed the best, with the best performance of diagnosis in AUC (0.951, 95% CI 0.925–0.978), sensitivity, accuracy, average precision, F1 score, negative predictive value, and excellent specificity and positive predictive value. The XGBoost model is high-performance and overcomes the shortcomings(long learning times and overfitting problems) of the gradient boosting machine(GBM) that has been used for diagnosis and prediction in multiple clinical scenarios for T2DM [[ 34 ]]. Among all syndromes, tongue and pulse characters from four main diagnostic procedures, slimy yellow tongue fur was the most important sign in dampness-heat pattern diagnosis, determined by machine learning in our study.…”

Section: Discussionmentioning

confidence: 99%

Application of machine learning in Chinese medicine differentiation of dampness-heat pattern in patients with type 2 diabetes mellitus

Liu¹,

Huang²,

Zhao³

et al. 2023

Heliyon

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“…The input of the binary classification model is an HLA-peptide pair, and the output of that is 1 or 0, where 1 means the peptide will bind to the HLA allele, and 0 means the peptide will not bind. Seven popular binary classifiers are used to establish the classification models, including logistic regression (LR) [ 39 ], support vector machine (SVM) [ 40 ], bagging classifier (Bagging) [ 41 ], extreme gradient boost (XGBoost) [ 42 ], k -nearest neighbor (KNN) [ 43 ], decision tree (Dtree) [ 44 ] and naive bayes (NB) [ 45 ].…”

Section: Methodsmentioning

confidence: 99%

HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction

Zhang

Zhu

et al. 2022

Briefings in Bioinformatics

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Human Leukocyte Antigen (HLA) is a type of molecule residing on the surfaces of most human cells and exerts an essential role in the immune system responding to the invasive items. The T cell antigen receptors may recognize the HLA-peptide complexes on the surfaces of cancer cells and destroy these cancer cells through toxic T lymphocytes. The computational determination of HLA-binding peptides will facilitate the rapid development of cancer immunotherapies. This study hypothesized that the natural language processing-encoded peptide features may be further enriched by another deep neural network. The hypothesis was tested with the Bi-directional Long Short-Term Memory-extracted features from the pretrained Protein Bidirectional Encoder Representations from Transformers-encoded features of the class I HLA (HLA-I)-binding peptides. The experimental data showed that our proposed HLAB feature engineering algorithm outperformed the existing ones in detecting the HLA-I-binding peptides. The extensive evaluation data show that the proposed HLAB algorithm outperforms all the seven existing studies on predicting the peptides binding to the HLA-A*01:01 allele in AUC and achieves the best average AUC values on the six out of the seven k-mers (k=8,9,...,14, respectively represent the prediction task of a polypeptide consisting of k amino acids) except for the 9-mer prediction tasks. The source code and the fine-tuned feature extraction models are available at http://www.healthinformaticslab.org/supp/resources.php.

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Design of intelligent diabetes mellitus detection system using hybrid feature selection based XGBoost classifier

Cited by 70 publications

References 45 publications

Improved Multiclassification of Schizophrenia Based on Xgboost and Information Fusion for Small Datasets

Improved Multiclassification of Schizophrenia Based on Xgboost and Information Fusion for Small Datasets

Application of machine learning in Chinese medicine differentiation of dampness-heat pattern in patients with type 2 diabetes mellitus

HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction

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