Particle Swarm Optimization Feature Selection for Breast Cancer Prediction

Nurhayati, .; Agustian, Fajar; Lubis, Muhammad Dzil Ikram

doi:10.1109/citsm50537.2020.9268865

Cited by 15 publications

(8 citation statements)

References 21 publications

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confidence: 64%

“…Iris results presented in Nasiri and Shakibian (2022) and Singaravelan et al (2018) for example, show the accuracies of 96% and 98%, while Wine results in (Deng et al, 2017; Nasiri & Shakibian, 2022) are 98.73% and 100%, respectively. Comparable performance also can be seen in Breast Cancer and Heart, for example in (Nurhayati, Agustian, & Lubis, 2020) (Breast Cancer—98.07%), (Islam et al, 2020) (Breast Cancer—97.14%), (Panup et al, 2022) (Heart—82.9%) and (Khanom et al, 2020) (Heart—86.89%). Finally, the performance of SVM with Cardiotocography is also compared, for example 74.92% in Amin et al (2019), 86.59% in Prasetyo et al (2021) and 84.38% in Parvathsuman et al (2019).…”

Section: Weighted Grcmentioning

confidence: 80%

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An evolving feature weighting framework for radial basis function neural network models

2022

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Via Granular Computing (GrC), one can create effective computational frameworks for obtaining information from data, motivated by the human perception of combining similar objects. Combining knowledge gained via GrC with a Fuzzy inference engine (Neural-Fuzzy) enable us to develop a transparent system. While weighting variables based on their importance during the iterative data granulation process has been proposed before (W-GrC), there is no work in the literature to demonstrate effectiveness and impact on Type-2 Fuzzy Logic systems (T2-FLS). The main contribution of this paper is to extend W-GrC, for the first time, to both Type-1 and Type-2 models known as Radial Basis Function Neural Network (RBFNN) and General Type-2 Radial Basis Function Neural Network (GT2-RBFNN). The proposed framework is validated using popular datasets: Iris, Wine, Breast Cancer, Heart and Cardiotocography. Results show that with the appropriate selection of feature weight parameter, the new computational framework achieves better classification accuracy outcomes. In addition, we also introduce in this research work an investigation on the modelling structure's interpretability (via Nauck's index) where it is shown that a good balance of interpretability and accuracy can be maintained.

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confidence: 64%

Section: Weighted Grcmentioning

confidence: 80%

An evolving feature weighting framework for radial basis function neural network models

2022

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“…We observe a decrease in the performance of ResNet trained from scratch when interpolation is included in the data augmentation, suggesting that interpolation corrupts local texture structures. S. Swetha Et.al [20] aim to create an affordable, multifunctional, personalized oral detecting equipment that processing aid and categorizes Network to classify oral disorders.. From the experimental results, it is observed that CNN performed well with an accuracy of 91% Nurhayati Et.al [21] present Particle Swarm Optimization (PSO) was used to enhance the effectiveness of different classifiers such as SVM, Naive Bayes, Logistic Regression, Decision Tree, and KNN. That classification model is used both to assess the efficiency level as well as a classifiers.…”

Section: Introductionmentioning

confidence: 99%

Oral Cancer Detection in its Nascent Stages Using 3D CNN: A Survey

Sagari¹,

Solanki²,

Goel³

et al. 2023

IJSREM

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Abstract—oral cavity cancer also called squamous cell carcinoma is the sixth most frequent cancer overall, it is a rising public health concern that is spreading across the globe. If not detected in earlier stages, it might be severe generally, and underlying lesions induce it. Therefore, it might be difficult to locate the lesion area and if not cured in its nascent stages, it might reach stage IV cancer, called the metastatic stage. To come up with a solution, we tend to use the three- dimensional CNN processes malignancies with image data and 3DCNN structures to classify benign and aggressive oral tumors. By identifying the kind of tumor region susing various kinds of knowledge here on periodic alterations in tumor enhancers, CNN was utilized in Test with visual categorization improvement rates. Analyzed, categorized, and future study areas are identified. The datasets utilized in the review articles are covered in detail, as are the pre- processing methods, the deep learning frameworks employed, etc. It presents a comparison of the same. Index Terms—Oral Cancer, Oral lesions, Oral Cavity, 3D CNN, Pre-processing, tumors, malignant, deep-learning

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“…Other scholars focus on the feature selection of data [18][19][20][21][22][23][24]. In the process of prediction, feature selection can eliminate irrelevant variables and redundant features to achieve effective dimensionality reduction and improve the accuracy of the algorithm [25].…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Prediction Based on Differential Privacy and Logistic Regression Optimization Model

Chen,

Wang,

Zhou

et al. 2023

Applied Sciences

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In order to improve the classification effect of the logistic regression (LR) model for breast cancer prediction, a new hybrid feature selection method is proposed to process the data, using the Pearson correlation test and the iterative random forest algorithm based on out-of-bag estimation (RF-OOB) to screen the optimal 17 features as inputs to the model. Secondly, the LR is optimized using the batch gradient descent (BGD-LR) algorithm to train the loss function of the model to minimize the loss. In order to protect the privacy of breast cancer patients, a differential privacy protection technology is added to the BGD-LR model, and an LR optimization model based on differential privacy with batch gradient descent (BDP-LR) is constructed. Finally, experiments are carried out on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Meanwhile, accuracy, precision, recall, and F1-score are selected as the four main evaluation indicators. Moreover, the hyperparameters of each model are determined by the grid search method and the cross-validation method. The experimental results show that after hybrid feature selection, the optimal results of the four main evaluation indicators of the BGD-LR model are 0.9912, 1, 0.9886, and 0.9943, in which the accuracy, recall, and F1-scores are increased by 2.63%, 3.41%, and 1.76%, respectively. For the BDP-LR model, when the privacy budget ε is taken as 0.8, the classification performance and privacy protection effect of the model reach an effective balance. At the same time, the four main evaluation indicators of the model are 0.9721, 0.9975, 0.9664, and 0.9816, which are improved by 1.58%, 0.26%, 1.81%, and 1.07%, respectively. Comparative analysis shows that the models of BGD-LR and BDP-LR constructed in this paper perform better than other classification models.

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Particle Swarm Optimization Feature Selection for Breast Cancer Prediction

Cited by 15 publications

References 21 publications

An evolving feature weighting framework for radial basis function neural network models

An evolving feature weighting framework for radial basis function neural network models

Oral Cancer Detection in its Nascent Stages Using 3D CNN: A Survey

Breast Cancer Prediction Based on Differential Privacy and Logistic Regression Optimization Model

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