RETRACTED ARTICLE: Early diagnose breast cancer with PCA-LDA based FER and neuro-fuzzy classification system

Radhakrishnan, P.; Jinny, S. Vinila

doi:10.1007/s12652-020-02395-z

Cited by 24 publications

(5 citation statements)

References 23 publications

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“…GWO FS + SVM 1.00 -1.00 -1.00 Mafarja et al [129] Hybridized WOA and SA 0.97 ----Ghosh et al [66] SMO-X FS + k-NN 1.00 ----Huang et al [98] GA-RBF SVM (small scale data) 0.98 --0.99 -RBF-SVM (large scale data) 0.99 --0.99 -Kumari and Singh [122] k-NN 0.99 ----Kumar et al [121] Lazy k-star/ Lazy IBK. 0.99 0.99 0.99 0.99 -Prakash and Rajkumar [165] HLFDA-T2FNN 0.98 ----Preetha and Jinny [166] PCA-LDA + ANNFIA 0.97 ----Sandhiya and Palani [186] ICRF-LCFS 0.94 ----Chatterjee et al [36] SSD-LAHC + k-NN 0.99 ----Ahmed et al [6] RTHS + k-NN 0.99 ----Guha et al [72] CPBGSA +MLP 0.99 ----Guha et al [73] ECWSA + k-NN 0.95 ----Ghosh et al [67] MRFO + k-NN One can also find a number of hybrid FS methods [6,36,67,72,73] where the researchers employed their method on WBC dataset. For example, Chatterjee et al [36] designed a hybrid FS method which improved the local search capability of the Social Ski Driver (SSD) algorithm with the help of the Late Acceptance Hill Climbing (LAHC) method.…”

Section: Diagnosis Report Based Methodsmentioning

confidence: 99%

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

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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.

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Section: Diagnosis Report Based Methodsmentioning

confidence: 99%

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

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“…Jenny and Preetha 19 used a method that combines Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to get rid of noise and get rid of features that didn’t belong in the breast cancer dataset. Alshareef et.…”

Section: Related Workmentioning

confidence: 99%

Advancing prostate cancer detection: a comparative analysis of PCLDA-SVM and PCLDA-KNN classifiers for enhanced diagnostic accuracy

Dubey,

Kumar

2023

Sci Rep

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This investigation aimed to assess the effectiveness of different classification models in diagnosing prostate cancer using a screening dataset obtained from the National Cancer Institute’s Cancer Data Access System. The dataset was first reduced using the PCLDA method, which combines Principal Component Analysis and Linear Discriminant Analysis. Two classifiers, Support Vector Machine (SVM) and k-Nearest Neighbour (KNN), were then applied to compare their performance. The results showed that the PCLDA-SVM model achieved an impressive accuracy rate of 97.99%, with a precision of 0.92, sensitivity of 92.83%, specificity of 97.65%, and F1 score of 0.93. Additionally, it demonstrated a low error rate of 0.016 and a Matthews Correlation Coefficient (MCC) and Kappa coefficient of 0.946. On the other hand, the PCLDA-KNN model also performed well, achieving an accuracy of 97.8%, precision of 0.93, sensitivity of 93.39%, specificity of 97.86%, an F1 score of 0.92, a high MCC and Kappa coefficient of 0.98, and an error rate of 0.006. In conclusion, the PCLDA-SVM method exhibited improved efficacy in diagnosing prostate cancer compared to the PCLDA-KNN model. Both models, however, showed promising results, suggesting the potential of these classifiers in prostate cancer diagnosis.

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“…PCA is the most widely used multivariate analysis technique, with applications in practically every scientific field. One of the main uses of PCA, which has been widely used by academics in a range of domains, is dimensionality reduction (Preetha and Vinila 2020). As its name implies, instead of studying every aspect of a problem, the PCA can help with some of the most crucial aspects and identify key components.…”

Section: Pcamentioning

confidence: 99%

Using the ordinal priority approach for selecting the contractor in construction projects

Alkaabi,

Raoof Mahjoob

2023

Organization, Technology and Management in Construction: An International Journal

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Effective decision-making process is the basis for successfully solving any engineering problem. Many decisions taken in the construction projects differ in their nature due to the complex nature of the construction projects. One of the most crucial decisions that might result in numerous issues over the course of a construction project is the selection of the contractor. This study aims to use the ordinal priority approach (OPA) for the contractor selection process in the construction industry. The proposed model involves two computer programs; the first of these will be used to evaluate the decision-makers/experts in the construction projects, while the second will be used to formulate the OPA mathematical model. The experts’ interview was used to identify the criteria of evaluation process of the decision-makers/experts, while Delphi survey with principal component analysis (PCA) was conducted to identify the required selection criteria of the construction projects contractors. The results illustrate that there are 20 criteria for selecting the construction contractor, and 7 criteria for evaluating the decision-makers/experts in the construction projects. Finally, the proposed model has been applied in a real construction project, and showed good results.

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RETRACTED ARTICLE: Early diagnose breast cancer with PCA-LDA based FER and neuro-fuzzy classification system

Cited by 24 publications

References 23 publications

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

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

Advancing prostate cancer detection: a comparative analysis of PCLDA-SVM and PCLDA-KNN classifiers for enhanced diagnostic accuracy

Using the ordinal priority approach for selecting the contractor in construction projects

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