Skin disease prediction using ensemble methods and a new hybrid feature selection technique

Verma, Anurag; Pal, Saurabh; Tiwari, B. B.

doi:10.1007/s42044-020-00058-y

Cited by 25 publications

(8 citation statements)

References 27 publications

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“…The result showed that the ensemble classifier was superior than the single classifier.Literature [29] proposed maximum relevance minimum redundancy and artificial neural network as the hybrid method, the ensemble method based on decision tree (DT) with the bagging method for the classification of brain tumor tissues. The result showed that it was more efficient to combine the hybrid feature selection and ensemble method, but their ensemble method only considered decision tree.Literature [30] combined three feature selection methods as the hybrid feature selection, which included chi-square, information gain and principal component analysis as the hybrid method. This study used three ensemble method to evaluate the six base classifiers, such as Gaussian Naïve Bayesian.…”

Section: Related Workmentioning

confidence: 99%

A Novel Hybrid Feature Selection and Ensemble Learning Framework for Unbalanced Cancer Data Diagnosis With Transcriptome and Functional Proteomic

et al. 2021

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The high dimension, high redundancy and class imbalance of cancer multiple omics data are the main challenges for cancer diagnosis. Existing studies have neglected the role of functional proteomics in the occurrence and development of cancer. In this study, a novel hybrid feature selection and ensemble learning framework, referred to as the three-stage feature selection and twice-competitional ensemble learning method (TSFS-TCEM), is proposed for cancer diagnosis. Firstly, we combine the transcriptome and functional proteomics data to construct a multi-omics data on breast cancer, which is the first time to apply these combined biological data for diagnosing breast cancer. Secondly, the proposed method introduces multiple models during the feature selection and diagnostic model construction. The three-stage feature selections integrate the features from different types of data and the twice-competitional ensemble learning framework resolves the data imbalance problem suffer from a single classifier. The TSFS-TCEM achieves a diagnostic accuracy of 99.64%, outperforming all compared methods. In addition, the 5-fold cross-validation sensitivity, specificity and F-Measure of the method are above 99.63%.

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Section: Related Workmentioning

confidence: 99%

A Novel Hybrid Feature Selection and Ensemble Learning Framework for Unbalanced Cancer Data Diagnosis With Transcriptome and Functional Proteomic

et al. 2021

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“…In 2020, Verma et al [ 32 ] proposed a newly proposed method of hybrid feature selection technique for evaluating the performance of base learners, and we find that the reduced data subset performed is higher than the whole data set. Also, Osubor et al [ 33 ] used an adaptive fuzzy neural inference system to predict postpartum depression.…”

Section: Related Workmentioning

confidence: 76%

A hybrid method for heart disease diagnosis utilizing feature selection based ensemble classifier model generation

Abdollahi

Nouri-Moghaddam

2022

Iran J Comput Sci

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Heart disease is one of the most complicated diseases, and it affects a large number of individuals throughout the world. In healthcare, particularly cardiology, early and accurate detection of cardiac disease is critical. The Heart Disease Data Set-UCI repository collects data on heart disease. The search space and complexity of the classification models are increased by this raw dataset, which contains redundant and inconsistent data. We need to eliminate the redundant and unnecessary elements from the data to improve classification accuracy. As a consequence, feature selection approaches might be useful for reducing the cost of diagnosis by identifying the most important qualities. This research developed an ensemble classification model based on a feature selection approach in which selected features play a role in classification. Accordingly, a classification approach was introduced using ensemble learning with a genetic algorithm, feature selection, and biomedical test values to diagnose heart disease. Based on the results, it is deduced that the benefits of using the feature selection method vary depending on the utilized machine learning technique. However, the best-proposed model based on the combination of genetic algorithm and the ensemble learning model has achieved an accuracy of 97.57% on the considered datasets. The suggested diagnosis system achieved better accuracy than previously proposed methods and can easily be implemented in healthcare to identify heart disease. Graphical abstract

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“…Ensemble learning is a machine learning approach that attempts to improve prediction performance by combining several weak learners into one powerful learner, which aims to reduce prediction generalization errors ( Harangi, 2018 ; Hera et al, 2022 ; Zaini and Awang, 2023 ). Verma et al built six different machine learning models and then developed an ensemble model using stacking and improved the performance of skin disease prediction with a final accuracy of 99.67% ( Verma et al, 2020 ). Abdollahi and Nouri-Moghaddam used the stacking ensemble method to predict diabetes and achieved a 98.8% accuracy in disease diagnosis ( Abdollahi and Nouri-Moghaddam, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population

Zhang,

et al. 2024

Front. Physiol.

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Objectives: An accurate prediction model for hyperuricemia (HUA) in adults remain unavailable. This study aimed to develop a stacking ensemble prediction model for HUA to identify high-risk groups and explore risk factors.Methods: A prospective health checkup cohort of 40899 subjects was examined and randomly divided into the training and validation sets with the ratio of 7:3. LASSO regression was employed to screen out important features and then the ROSE sampling was used to handle the imbalanced classes. An ensemble model using stacking strategy was constructed based on three individual models, including support vector machine, decision tree C5.0, and eXtreme gradient boosting. Model validations were conducted using the area under the receiver operating characteristic curve (AUC) and the calibration curve, as well as metrics including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. A model agnostic instance level variable attributions technique (iBreakdown) was used to illustrate the black-box nature of our ensemble model, and to identify contributing risk factors.Results: Fifteen important features were screened out of 23 clinical variables. Our stacking ensemble model with an AUC of 0.854, outperformed the other three models, support vector machine, decision tree C5.0, and eXtreme gradient boosting with AUCs of 0.848, 0.851 and 0.849 respectively. Calibration accuracy as well as other metrics including accuracy, specificity, negative predictive value, and F1 score were also proved our ensemble model’s superiority. The contributing risk factors were estimated using six randomly selected subjects, which showed that being female and relatively younger, together with having higher baseline uric acid, body mass index, γ-glutamyl transpeptidase, total protein, triglycerides, creatinine, and fasting blood glucose can increase the risk of HUA. To further validate our model’s applicability in the health checkup population, we used another cohort of 8559 subjects that also showed our ensemble prediction model had favorable performances with an AUC of 0.846.Conclusion: In this study, the stacking ensemble prediction model for HUA was developed, and it outperformed three individual models that compose it (support vector machine, decision tree C5.0, and eXtreme gradient boosting). The contributing risk factors were identified with insightful ideas.

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Skin disease prediction using ensemble methods and a new hybrid feature selection technique

Cited by 25 publications

References 27 publications

A Novel Hybrid Feature Selection and Ensemble Learning Framework for Unbalanced Cancer Data Diagnosis With Transcriptome and Functional Proteomic

A Novel Hybrid Feature Selection and Ensemble Learning Framework for Unbalanced Cancer Data Diagnosis With Transcriptome and Functional Proteomic

A hybrid method for heart disease diagnosis utilizing feature selection based ensemble classifier model generation

Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population

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