Prediction of erythemato squamous-disease using ensemble learning framework

Igodan, Efosa Charles; Obe, Olumide Olayinka; Thompson, Aderonke Favour-Bethy; Owolafe, Otasowie

doi:10.1049/pbhe050e_ch7

Cited by 3 publications

(6 citation statements)

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“…Stacking can combine predictions from several base models trained on distinct subsets or representations of the data, exploiting the strengths of each model while limiting the impact of data heterogeneity. It can address class imbalance issues in imbalanced datasets by optimising performance on minority classes and incorporating models with various uncertainty handling strategies, such as imputation approaches or robust estimation methods [ 4 , 8 , 37 ]. Regardless of these advantages, like other machine learning methods, the performance of the stacking approach can be affected by various factors, including dataset attributes, clinical features, and modelling choices.…”

Section: Resultsmentioning

confidence: 99%

“…Stacking is an integrated approach that uses the metalearning model to integrate the output of base models. If the final decision element is a linear model, the stacking is also known as “model blending” or just “blending” [ 8 ]. Stacking involves fitting multiple different types of models to the same data and then using a different model to determine how to combine the results most efficiently [ 3 ].…”

Section: Ensemble Learningmentioning

confidence: 99%

“…The fundamental drawback of bagging is that it increases model accuracy at the expense of interpretability [ 8 ]. For example, if a single tree were used as the base model, the diagram would be more appealing and easier to understand, but when bagging is employed, this interpretability is lost.…”

Section: Figurementioning

confidence: 99%

“…In the Voting Classifier, there are two types of voting: hard voting, in which each The dataset is randomly divided into n equal sections in this procedure. One set is utilised for testing and the rest for training in the nth-fold cross-validation [8]. We derive the predictions of various learning models using these training testing pair subsets, which are then used as metadata to build the metamodel [18].…”

Section: Votingmentioning

confidence: 99%

“…Using these databases, researchers create disease prediction models for decision-making systems, allowing for better disease prediction and treatment at an early stage. Early diagnosis and timely treatment are the most effective ways to lower disease-related mortality rates [ 8 ]. As a result, most medical scientists are drawn to emerging machine learning-based predictive model technologies for disease prediction.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble Learning for Disease Prediction: A Review

et al. 2023

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Machine learning models are used to create and enhance various disease prediction frameworks. Ensemble learning is a machine learning technique that combines multiple classifiers to improve performance by making more accurate predictions than a single classifier. Although numerous studies have employed ensemble approaches for disease prediction, there is a lack of thorough assessment of commonly used ensemble approaches against highly researched diseases. Consequently, this study aims to identify significant trends in the performance accuracies of ensemble techniques (i.e., bagging, boosting, stacking, and voting) against five hugely researched diseases (i.e., diabetes, skin disease, kidney disease, liver disease, and heart conditions). Using a well-defined search strategy, we first identified 45 articles from the current literature that applied two or more of the four ensemble approaches to any of these five diseases and were published in 2016–2023. Although stacking has been used the fewest number of times (23) compared with bagging (41) and boosting (37), it showed the most accurate performance the most times (19 out of 23). The voting approach is the second-best ensemble approach, as revealed in this review. Stacking always revealed the most accurate performance in the reviewed articles for skin disease and diabetes. Bagging demonstrated the best performance for kidney disease (five out of six times) and boosting for liver and diabetes (four out of six times). The results show that stacking has demonstrated greater accuracy in disease prediction than the other three candidate algorithms. Our study also demonstrates variability in the perceived performance of different ensemble approaches against frequently used disease datasets. The findings of this work will assist researchers in better understanding current trends and hotspots in disease prediction models that employ ensemble learning, as well as in determining a more suitable ensemble model for predictive disease analytics. This article also discusses variability in the perceived performance of different ensemble approaches against frequently used disease datasets.

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Section: Resultsmentioning

confidence: 99%

Section: Ensemble Learningmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Votingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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