Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction

Abdollahi, Jafar; Nouri-Moghaddam, Babak

doi:10.1007/s42044-022-00100-1

Cited by 56 publications

(35 citation statements)

References 74 publications

(46 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ensemble models used in [ 10 , 18 , 19 , 20 ], showed a better performance than any single ML algorithm, that is why the usage of ensemble models to diagnose disease has increased. In [ 21 ], the researchers built a decision-level fusion model to predict heart disease, which was further improved by applying the weighted score fusion [ 7 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, the interpretability of the ML models was not shown. Abdollahi and Nouri-Moghaddam [ 19 ] introduced the stacked generalization and they used an ensemble approach with a genetic algorithm to diagnose the disease with a promising outcome. An accuracy of 98% was achieved using 70% of the data for training.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The lack of preprocessing and imbalanced classes in the dataset resulted in a poor accuracy in these studies [ 10 , 18 , 19 ]. These limitations were overcome by Fitriyani et al [ 20 ] who developed an ensemble model to predict the diabetes disease.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

An Ensemble Approach for the Prediction of Diabetes Mellitus Using a Soft Voting Classifier with an Explainable AI

Kibria

Nahiduzzaman

Goni

et al. 2022

Sensors

View full text Add to dashboard Cite

Diabetes is a chronic disease that continues to be a primary and worldwide health concern since the health of the entire population has been affected by it. Over the years, many academics have attempted to develop a reliable diabetes prediction model using machine learning (ML) algorithms. However, these research investigations have had a minimal impact on clinical practice as the current studies focus mainly on improving the performance of complicated ML models while ignoring their explainability to clinical situations. Therefore, the physicians find it difficult to understand these models and rarely trust them for clinical use. In this study, a carefully constructed, efficient, and interpretable diabetes detection method using an explainable AI has been proposed. The Pima Indian diabetes dataset was used, containing a total of 768 instances where 268 are diabetic, and 500 cases are non-diabetic with several diabetic attributes. Here, six machine learning algorithms (artificial neural network (ANN), random forest (RF), support vector machine (SVM), logistic regression (LR), AdaBoost, XGBoost) have been used along with an ensemble classifier to diagnose the diabetes disease. For each machine learning model, global and local explanations have been produced using the Shapley additive explanations (SHAP), which are represented in different types of graphs to help physicians in understanding the model predictions. The balanced accuracy of the developed weighted ensemble model was 90% with a F1 score of 89% using a five-fold cross-validation (CV). The median values were used for the imputation of the missing values and the synthetic minority oversampling technique (SMOTETomek) was used to balance the classes of the dataset. The proposed approach can improve the clinical understanding of a diabetes diagnosis and help in taking necessary action at the very early stages of the disease.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

An Ensemble Approach for the Prediction of Diabetes Mellitus Using a Soft Voting Classifier with an Explainable AI

Kibria

Nahiduzzaman

Goni

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…In essence, this process assists in determining reasons for misclassifications; thus, they can be corrected via employing essential measures. 25,26,27,28 Accuracy: demonstrates the "correct predictions made" number by the class, which is divided by the "total predictions made" number by the equivalent class.…”

Section: Performance Metricsmentioning

confidence: 99%

Detection of Metastatic Breast Cancer from Whole-Slide Pathology Images Using an Ensemble Deep-Learning Method

et al. 2022

Self Cite

View full text Add to dashboard Cite

Background: Metastasis is the main cause of death toll among breast cancer patients. Since current approaches for diagnosis of lymph node metastases are time-consuming, deep learning (DL) algorithms with more speed and accuracy are explored for effective alternatives. Methods: A total of 220025 whole-slide pictures from patients’ lymph nodes were classified into two cohorts: testing and training. For metastatic cancer identification, we employed hybrid convolutional network models. The performance of our diagnostic system was verified using 57458 unlabeled images that utilized criteria that included accuracy, sensitivity, specificity, and P-value. Results: The DL-based system that was automatically and exclusively capable of quantifying and identifying metastatic lymph nodes was engineered. Quantification was made with 98.84% accuracy. Moreover, the precision of VGG16 and Recall was 92.42% and 91.25%, respectively. Further experiments demonstrated that metastatic cancer differentiation levels could influence the recognition performance. Conclusion: Our engineered diagnostic complex showed an elevated level of precision and efficiency for lymph node diagnosis. Our innovative DL-based system has a potential to simplify pathological screening for metastasis in breast cancer patients.

show abstract

“…Embedded methods combine filter qualities and wrapper methods. Algorithms do this with internal feature selection methods [ 40 – 47 ]. The various techniques used in this article are described in Table 1 .…”

Section: Related Workmentioning

confidence: 99%

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

Abdollahi

Nouri-Moghaddam

2022

Iran J Comput Sci

Self Cite

View full text Add to dashboard Cite

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

show abstract

Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction

Cited by 56 publications

References 74 publications

An Ensemble Approach for the Prediction of Diabetes Mellitus Using a Soft Voting Classifier with an Explainable AI

An Ensemble Approach for the Prediction of Diabetes Mellitus Using a Soft Voting Classifier with an Explainable AI

Detection of Metastatic Breast Cancer from Whole-Slide Pathology Images Using an Ensemble Deep-Learning Method

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

Contact Info

Product

Resources

About