Multistage Ensemble Learning Model With Weighted Voting and Genetic Algorithm Optimization Strategy for Detecting Chronic Obstructive Pulmonary Disease

Dhar, Joy

doi:10.1109/access.2021.3067949

Cited by 14 publications

(27 citation statements)

References 18 publications

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“…ML and DL-enabled methodologies are increasing rapidly in the health industry and may adequately early predict several diseases [7]. In this concern, to correctly and early predict people with diabetes and HCC diseases, various researchers have generated infinite ML and DL-relevant approaches to support clinical decision-making.…”

Section: Relevant Work and Research Gapmentioning

confidence: 99%

“…HCC dataset is exhibited in Appendix 1. This dataset comprises 10.22% of missing data of the whole dataset [7]. This dataset comprises 0 for the dead class and 1 for the alive class, in which 63 sufferers belong to the dead class and 102 sufferers belong to the alive class.…”

Section: ) Hcc Datasetmentioning

confidence: 99%

“…IMPUTATION TECHNIQUE: In this paper, the KNN imputation technique is employed on the HCC dataset, in which forty-four features comprise missing data presented in Appendix 1. Regarding this matter, filling the missing value in the HCC dataset, we implemented the KNN missing data imputation technique [4,7]. This approach can preserve the actual data distribution after determining the fittest K value [4,7,10].…”

Section:  Missing Data Imputation Using Knnmentioning

confidence: 99%

“…However, such above-specified methodologies have some lags due to generating inefficient and inaccurate outcomes. So, it requires several improvements in enhancing the accuracy and reliability while predicting such diseases because each machine learning researcher's foremost objective is to give a well-built and accurate auspicious method with the best performances [7]. The main reason for doing this is that research on healthcare data is the most crucial problem due to its close relation with individuals' lives [7].…”

Section: Introductionmentioning

confidence: 99%

“…So, it requires several improvements in enhancing the accuracy and reliability while predicting such diseases because each machine learning researcher's foremost objective is to give a well-built and accurate auspicious method with the best performances [7]. The main reason for doing this is that research on healthcare data is the most crucial problem due to its close relation with individuals' lives [7]. In this regard, this paper proposes a multi-tier weighted ensemble learning model (MTWEL) to solve the above-stated problems and help physicians reduce their workload, provide the proper medication, and precise early prediction of such diseases with minimal error rate terms of treatment.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Tier Ensemble Learning Model With Neighborhood Component Analysis to Predict Health Diseases

Dhar¹,

Ayele

2021

IEEE Access

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Various well-known health diseases affect millions of people worldwide. Sometimes in the early stage, the clinicians may not recognize several clinical symptoms due to lack of their symptoms reflection or anything else. Thus, such diseases are not easier to identify. There may have chances to grow these illnesses and affected millions of people worldwide. The risk factor of such diseases severity can be lessened, notably whenever an accurate early prediction is possible. This study presents an innovative multi-tier weighted ensemble learning model (MTWEL) for predicting several diseases such as diabetes and hepatocellular carcinoma (HCC) and, therefore, reduces such above-said problems from the sufferers and lessens the chances of mortality. In the MTWEL model, we have utilized two lists of base classifiers in which six various machine learning (ML) classifiers are assigned in each list to develop two weighted ensemble learning (EL) models and combine them to form the proposed model by employing a weighted voting approach. In the MTWEL model, the parameters of all employed classifiers are tuned through the genetic algorithm-enabled hyperparameter optimization technique to form the optimized base models. The weight of each chosen optimized base model and generated EL model(s) is calculated using Matthews correlation coefficient value with the optimized weight value. In this study, neighborhood component analysis is employed to reduce the dimension of the given input dataset. The suggested model's experimental outcomes are conducted on two real-world datasets to exhibit its performance. The suggested approach receives the best result in AUC values: 1.0 and 1.0, F1-score values: 0.9957 and 0.9947, and accuracy values: 0.9952 and 0.9929. Such outcomes in the form of performance exhibit that the proposed model is the best-suited model to predict several diseases than other techniques, and hence it helps clinicians make accurate decisions.

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Section: Relevant Work and Research Gapmentioning

confidence: 99%

Section: ) Hcc Datasetmentioning

confidence: 99%