Comprehensive study on ensemble classification for medical  applications

Rosly, Rosaida; Makhtar, Mokhairi; Awang, Mohd Khalid; Awang, Mohd Isha; Rahman, Mohd Nordin Abdul; Mahdin, Hairulnizam

doi:10.14419/ijet.v7i2.14.12822

Cited by 4 publications

(3 citation statements)

References 15 publications

(21 reference statements)

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“…Subsequently, the accumulation of correct predictions was divided by the total dataset. It is determined using Equation 1 [14]:…”

Section: Table 1 Confusion Matrixmentioning

confidence: 99%

Multi-classifier models to improve the accuracy of fish landing application

2024

IJATEE

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Despite the numerous fish classification systems developed over the years, they often suffer from poor prediction accuracy, necessitating further improvement. This study addresses this issue by comparing the performance of different classifiers on fish landing datasets (2005-2019) obtained from the Department of Fisheries Malaysia (DOFM). The focus is on the East Coast of Peninsular Malaysia. The classifiers evaluated include Sequential minimal optimization (SMO), naïve Bayes (NB), multi-layer perception (MLP), instance-based for k-nearest neighbor (IBK), and random forest (RF). The performance of each classifier is assessed using classification accuracy and confusion matrix metrics, employing a 10-fold cross-validation method. Additionally, a multi-classification technique is applied to enhance the accuracy of individual classifiers and determine the most effective approach for generating an accurate dataset. The study reveals that the combinations RF+SMO+NB+MLP and SMO+RF+NB+MLP outperform single classifiers and other fusion methods, achieving the highest accuracy at 80.95%. This indicates that a multi-classifier approach can significantly enhance the performance of individual classifiers. The findings highlight the effectiveness of the multi-classifier approach in improving prediction accuracy for fish classification. The identified combinations, RF+SMO+NB+MLP and SMO+RF+NB+MLP, demonstrate superior performance and can serve as a robust methodology for fish landing classification in the context of the East Coast of Peninsular Malaysia. Further research and implementation of such multi-classifier approaches could contribute to more accurate and reliable fish classification systems.

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“…Subsequently, the accumulation of correct predictions was divided by the total dataset. It is determined using Equation 1 [14]:…”

Section: Table 1 Confusion Matrixmentioning

confidence: 99%

Multi-classifier models to improve the accuracy of fish landing application

2024

IJATEE

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“…In the ensemble approach, various classification techniques are combined in order to improve the accuracy of the entire model. Various researches have been conducted for developing efficient systems using ensemble approach [20], [21], [22], [23]. In this project, we have used SVM, Naïve Bayes, KNN and Decision Tree for developing the ensemble classifier.…”

Section: Ensemble Classifiermentioning

confidence: 99%

Predicting hyperlipidemia using enhanced ensemble classifier

Lakshmi

Vadivu

Subramanian

2018

IJET

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Advancement in medical technology has resulted in bulk creation of electronic medical health records. These health records contain valuable data which are not fully utilized. Efficient usage of data mining techniques helps in discovering potentially relevant facts from medical records. Classification plays an important role in disease prediction. In this paper we developed a prediction model for predicting hyperlipidemia based on ensemble classification. Support Vector Machine, Naïve Bayes Classifier, KNN Classifier and Decision Tree method are combined for developing the ensemble classifier. Performance of each classifier is evaluated separately. An overall accuracy of 97.07% has been obtained by using ensemble approach which is better than the performance of each classifier.

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“…The base models in bagging and stacking must be homogenous, but in stacking it could be heterogeneous. Rather than relying solely on the output of a single model, these techniques guarantee the delivery of more accurate and trustworthy results from multiple models [14] [15]. Few studies have used ensemble methods for developing brain stroke prediction models, despite the value of using a stacking ensemble classifier to build predictive models with trustworthy outcomes in a variety of fields, including medical and natural phenomena [16] [17] [18].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Ensemble Classifier for Prediction of Brain Strokes

Mostafa¹,

Elzanfaly²,

Yakoub³

2022

IJACSA

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Brain Strokes are considered one of the deadliest brain diseases due to their sudden occurrence, so predicting their occurrence and treating the factors may reduce their risk. This paper aimed to propose a brain stroke prediction model using machine learning classifiers and a stacking ensemble classifier. The smote technique was employed for data balancing, and the standardization technique was for data scaling. The classifiers' best parameters were chosen using the hyperparameter tuning technique. The proposed stacking prediction model was created by combining Random Forest (RF), K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Naive Bayes (NB) as base classifiers, and meta learner was chosen to be Random Forest. The performance of the proposed stacking model has been evaluated using Accuracy, Precision, Recall, and F1 score. In addition, the Matthews Correlation Coefficient (MCC) has been also used for more reliable evaluation when having an unbalanced dataset, which is the case in most medical datasets. The results demonstrate that the proposed stacking model outperforms the standalone classifiers by achieving an accuracy of 97% and an MCC value of 94%.

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Comprehensive study on ensemble classification for medical applications

Cited by 4 publications

References 15 publications

Multi-classifier models to improve the accuracy of fish landing application

Multi-classifier models to improve the accuracy of fish landing application

Predicting hyperlipidemia using enhanced ensemble classifier

A Machine Learning Ensemble Classifier for Prediction of Brain Strokes

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