An Ensemble Approach for Handling Class Imbalanced Disease Datasets

Shaw, Sayan Surya; Ahmed, Shameem; Malakar, Samir; Sarkar, Ram

doi:10.1007/978-981-33-4087-9_30

Cited by 3 publications

(6 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This was based on the hypothesis that a good choice for a missing value would be the one that can reconstruct itself from the autoencoder. Recently, Shaw et al [202,203] used methods to handle imbalance class problems and subsequently improved the cancer prediction performance. In the work [203], the authors proposed an ensemble approach to handle the class problem while in [202] they solved the problem using an evolutionary algorithm.…”

Section: Diagnosis Report Based Methodsmentioning

confidence: 99%

“…Recently, Shaw et al [202,203] used methods to handle imbalance class problems and subsequently improved the cancer prediction performance. In the work [203], the authors proposed an ensemble approach to handle the class problem while in [202] they solved the problem using an evolutionary algorithm. In [202], the ring theory-based algorithm was hybridized with the PSO algorithm to select the near-optimal majority class samples from the training set.…”

Section: Diagnosis Report Based Methodsmentioning

confidence: 99%

“…Significant research developments with greater focus on the data preprocessing like missing value imputation [39,43,227], class imbalance handling [54,202,203], outlier removal [102], and dimensionality reduction [204,221] have taken place over the years. We have described a few important works that dealt with such processing to improve prediction performance.…”

Section: Diabetes Prediction Methodsmentioning

confidence: 99%

“…After using the X-square filter FS, the authors detected the presence of disease using the modified redial basis kernel of SVM. It is to be noted that recently, Shaw et al [202,203] proposed two new methods (ensemble method [203] and RTPSO for majority sample selection [202]) for better handling imbalance class problem in a dataset.…”

Section: Diabetes Prediction Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Malakar¹,

Roy

Das

et al. 2022

Arch Computat Methods Eng

Self Cite

View full text Add to dashboard Cite

Disease prediction from diagnostic reports and pathological images using artificial intelligence (AI) and machine learning (ML) is one of the fastest emerging applications in recent days. Researchers are striving to achieve near-perfect results using advanced hardware technologies in amalgamation with AI and ML based approaches. As a result, a large number of AI and ML based methods are found in the literature. A systematic survey describing the state-of-the-art disease prediction methods, specifically chronic disease prediction algorithms, will provide a clear idea about the recent models developed in this field. This will also help the researchers to identify the research gaps present there. To this end, this paper looks over the approaches in the literature designed for predicting chronic diseases like Breast Cancer, Lung Cancer, Leukemia, Heart Disease, Diabetes, Chronic Kidney Disease and Liver Disease. The advantages and disadvantages of various techniques are thoroughly explained. This paper also presents a detailed performance comparison of different methods. Finally, it concludes the survey by highlighting some future research directions in this field that can be addressed through the forthcoming research attempts.

show abstract

Section: Diagnosis Report Based Methodsmentioning

confidence: 99%

Section: Diagnosis Report Based Methodsmentioning

confidence: 99%

Section: Diabetes Prediction Methodsmentioning

confidence: 99%

Section: Diabetes Prediction Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Malakar¹,

Roy

Das

et al. 2022

Arch Computat Methods Eng

Self Cite

View full text Add to dashboard Cite

show abstract

“…Meanwhile, a significant challenge in applying machine learning algorithms for medical diagnosis is the imbalanced class problem [ 20 , 21 ]. Most ML classifiers underperform when trained with imbalanced datasets.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Method with Filter-Based Feature Selection for Improved Prediction of Chronic Kidney Disease

et al. 2022

View full text Add to dashboard Cite

The high prevalence of chronic kidney disease (CKD) is a significant public health concern globally. The condition has a high mortality rate, especially in developing countries. CKD often go undetected since there are no obvious early-stage symptoms. Meanwhile, early detection and on-time clinical intervention are necessary to reduce the disease progression. Machine learning (ML) models can provide an efficient and cost-effective computer-aided diagnosis to assist clinicians in achieving early CKD detection. This research proposed an approach to effectively detect CKD by combining the information-gain-based feature selection technique and a cost-sensitive adaptive boosting (AdaBoost) classifier. An approach like this could save CKD screening time and cost since only a few clinical test attributes would be needed for the diagnosis. The proposed approach was benchmarked against recently proposed CKD prediction methods and well-known classifiers. Among these classifiers, the proposed cost-sensitive AdaBoost trained with the reduced feature set achieved the best classification performance with an accuracy, sensitivity, and specificity of 99.8%, 100%, and 99.8%, respectively. Additionally, the experimental results show that the feature selection positively impacted the performance of the various classifiers. The proposed approach has produced an effective predictive model for CKD diagnosis and could be applied to more imbalanced medical datasets for effective disease detection.

show abstract