k-best feature selection and ranking via stochastic approximation

Akman, David; Malekipirbazari, Milad; Yenice, Zeren D.; Yeo, Anders; Adhikari, Niranjan; Wong, Yong Kai; Abbasi, Babak; Gümüş, Alev Taşkın

doi:10.1016/j.eswa.2022.118864

Cited by 15 publications

(5 citation statements)

References 45 publications

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“…Variance threshold was used to remove features with variance <0.8. Secondly, the k-best method ( 21 ) was employed to remove features with P-value >0.05. To establish a more refined model, the least absolute shrinkage and selection operator (LASSO, version 1.0.2) algorithm was used to formulate a penalty function to compress select regression coefficients ( 22 ).…”

Section: Methodsmentioning

confidence: 99%

CT‑based radiomics analysis of consolidation characteristics in differentiating pulmonary disease of non‑tuberculous mycobacterium from pulmonary tuberculosis

Yan,

Zhao,

Kong

et al. 2024

Exp Ther Med

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Global incidence rate of non-tuberculous mycobacteria (NTM) pulmonary disease has been increasing rapidly. In some countries and regions, its incidence rate is higher than that of tuberculosis. It is easily confused with tuberculosis. The topic of this study is to identify two diseases using CT radioomics. The aim in the present study was to investigate the value of CT-based radiomics to analyze consolidation features in differentiation of non-tuberculous mycobacteria (NTM) from pulmonary tuberculosis (TB). A total of 156 patients (75 with NTM pulmonary disease and 81 with TB) exhibiting consolidation characteristics in Shandong Public Health Clinical Center were retrospectively analyzed. Subsequently, 305 regions of interest of CT consolidation were outlined. Using a random number generated via a computer, 70 and 30% of consolidations were allocated to the training and the validation cohort, respectively. By means of variance threshold, when investigating the effective radiomics features, SelectKBest and the least absolute shrinkage and selection operator regression method were employed for feature selection and combined to calculate the radiomics score. K-nearest neighbor (KNN), support vector machine (SVM) and logistic regression (LR) were used to analyze effective radiomics features. A total of 18 patients with NTM pulmonary disease and 18 with TB possessing consolidation characteristics in Jinan Infectious Disease Hospital were collected for external validation of the model. A total of three methods was used in the selection of 52 optimal features. For KNN, the area under the curve (AUC; sensitivity, specificity) for the training and validation cohorts were 0.98 (0.93, 0.94) and 0.90 (0.88, 083), respectively; for SVM, AUC was 0.99 (0.96, 0.96) and 0.92 (0.86, 0.85) and for LR, AUC was 0.99 (0.97, 0.97) and 0.89 (0.88, 0.85). In the external validation cohort, AUC values of models were all >0.84 and LR classifier exhibited the most significant precision, recall and F1 score (0.87, 0.94 and 0.88, respectively). LR classifier possessed the best performance in differentiating diseases. Therefore, CT-based radiomics analysis of consolidation features may distinguish NTM pulmonary disease from TB.

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Section: Methodsmentioning

confidence: 99%

CT‑based radiomics analysis of consolidation characteristics in differentiating pulmonary disease of non‑tuberculous mycobacterium from pulmonary tuberculosis

Yan,

Zhao,

Kong

et al. 2024

Exp Ther Med

View full text Add to dashboard Cite

show abstract

“…Due to the lack of universal knowledge regarding LC, the C classifier is trained on the dataset and measures the value of 𝑦 𝐶 (X′, Y), where 𝑦 𝐶 = 𝐿 𝐶 plus. The non-empty feature set X is specified as the solution to the wrapper-based FS problem [29].…”

Section: K-best Feature Selection Methodsmentioning

confidence: 99%

Accurate AI-Based Chatbot to Diagnose Heart Diseases Pre-Human Doctor Consultation

Majeed,

Hardan,

Hardan

et al. 2024

RIA

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Early diagnosis of heart disorders is an essential issue since they might be life-threatening conditions that call for urgent medical care. Chatbots as a user-friendly technology can be used to deliver general assistance and information anywhere anytime. However, the decision-making accuracy is one of the key challenges of Chatbots. In this paper, a medical Chatbot is developed for the purpose of early cardiac disease diagnosis based on given symptoms. An approved dataset known as the Cleveland Heart Disease dataset is utilized in this work. Hence, for accurate decision-making, three machine learning algorithms Support Vector Machine (SVM), and Logistic Regression (LR), and Extreme Gradient Boosting (XGBoost) are compared and tested to nominate the best algorithms for heart health disorders detection. Three simulation scenarios are applied to test the performance of each algorithm using first all features of the dataset, then the k-best feature selection method, and the application of Grid Search (GS) optimization algorithms. The performance evaluation shows that the prediction accuracy of the proposed XGBoost algorithm outweighs other algorithms.

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“…Feature selection aims to obtain a minimal, informative subset, excluding irrelevant or highly correlated features [28]. The process involves selecting k-best features, ranging from 5 to 15 features with an increment of 5, denoted as k ∈ {5, 10, 15} [29]. Finally, data normalization, utilizing Z-Score transformation, ensures consistent ranges between data points [27].…”

Section: Preprocessing Datamentioning

confidence: 99%

Implementation of Feature Selection Strategies to Enhance Classification Using XGBoost and Decision Tree

Nadya,

Ferdiansyah,

Nastiti

et al. 2024

SJI

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Purpose: Grades in the world of education are often a benchmark for students to be considered successful or not during the learning period. The facilities and teaching staff provided by schools with the same portion do not make student grades the same, the value gap is still found in every school. The purpose of this research is to produce a better accuracy rate by applying feature selection Information Gain (IG), Recursive Feature Elimination (RFE), Lasso, and Hybrid (RFE + Mutual Information) using XGBoost and Decision Tree models.Methods: This research was conducted using 649 Portuguese course student data that had been pre-processed according to data requirements, then, feature selection was carried out to select features that affect the target, after that all data can be classified using XGBoost and Decision tree, finally evaluating and displaying the results. Results: The results showed that feature selection Information Gain combined with the XGBoost algorithm has the best accuracy results compared to others, which is 81.53%.Novelty: The contribution of this research is to improve the classification accuracy results of previous research by using 2 traditional machine learning algorithms and some feature selection.

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k-best feature selection and ranking via stochastic approximation

Cited by 15 publications

References 45 publications

CT‑based radiomics analysis of consolidation characteristics in differentiating pulmonary disease of non‑tuberculous mycobacterium from pulmonary tuberculosis

CT‑based radiomics analysis of consolidation characteristics in differentiating pulmonary disease of non‑tuberculous mycobacterium from pulmonary tuberculosis

Accurate AI-Based Chatbot to Diagnose Heart Diseases Pre-Human Doctor Consultation

Implementation of Feature Selection Strategies to Enhance Classification Using XGBoost and Decision Tree

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