Curvature-based feature selection with application in classifying electronic health records

Zuo, Zheming; Li, Jie; Han, XW; Moubayed, Noura Al

doi:10.1016/j.techfore.2021.121127

Cited by 15 publications

(13 citation statements)

References 42 publications

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“…Other models have been developed for breast cancer prediction by utilizing the Coimbra Breast Cancer dataset (CBCD). Zuo et al [11] suggested curvature-based feature selection (CFS) paired with a fuzzy inference system (TSK+) as an effective filter-based feature selection technique. To predict the selected and normalized features, the proposed model was compared with other classifiers, namely KNN, AdaBoost, back-propagation neural network (BPNN), RF, linear SVM, Gaussian naïve Bayes (GNB), LR, and decision tree (DT).…”

Section: Related Workmentioning

confidence: 99%

“…Early detection of disease can be achieved by developing a prediction model so that the patient will get better treatment. Machine-learning-based models have been utilized in previous studies for detecting breast cancer and showed significant performance [9][10][11][12][13][14]. Support vector machine (SVM) is an ML model that divides instances of each class from the others by locating the linear optimum hyperplane after nonlinearly mapping the original data into a high-dimensional feature space.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

et al. 2022

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Developing a prediction model from risk factors can provide an efficient method to recognize breast cancer. Machine learning (ML) algorithms have been applied to increase the efficiency of diagnosis at the early stage. This paper studies a support vector machine (SVM) combined with an extremely randomized trees classifier (extra-trees) to provide a diagnosis of breast cancer at the early stage based on risk factors. The extra-trees classifier was used to remove irrelevant features, while SVM was utilized to diagnose the breast cancer status. A breast cancer dataset consisting of 116 subjects was utilized by machine learning models to predict breast cancer, while the stratified 10-fold cross-validation was employed for the model evaluation. Our proposed combined SVM and extra-trees model reached the highest accuracy up to 80.23%, which was significantly better than the other ML model. The experimental results demonstrated that by applying extra-trees-based feature selection, the average ML prediction accuracy was improved by up to 7.29% as contrasted to ML without the feature selection method. Our proposed model is expected to increase the efficiency of breast cancer diagnosis based on risk factors. In addition, we presented the proposed prediction model that could be employed for web-based breast cancer prediction. The proposed model is expected to improve diagnostic decision-support systems by predicting breast cancer disease accurately.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

et al. 2022

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“…• Deployed a Curvature-based Feature Selection (CFS) method [25] to reduce the space and time complexity of the proposed fuzzy inference system;…”

Section: Predicted Labelmentioning

confidence: 99%

“…Feature Selection (FS) aims to select a subset of the most relevant attributes for the use of model construction from the given dataset [25]. In particular, FS methods identify the feature-wise importance for a given problem, thus helping select the most relevant (or discriminative) features.…”

Section: Curvature-based Feature Selectionmentioning

confidence: 99%

“…In particular, FS methods identify the feature-wise importance for a given problem, thus helping select the most relevant (or discriminative) features. Curvature-based FS (CFS) method [25] was developed to rank the weights (importance) of features by calculating the averaged Menger Curvature [26] of each feature, which can be summarized into the following three steps:…”

Section: Curvature-based Feature Selectionmentioning

confidence: 99%

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Seen to Unseen: When Fuzzy Inference System Predicts IoT Device Positioning Labels That Had Not Appeared in Training Phase

Han

Zuo

et al. 2022

Proceedings of the 7th International Conference on Internet of Things, Big Data and Security

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Situating at the core of Artificial Intelligence (AI), Machine Learning (ML), and more specifically, Deep Learning (DL) have embraced great success in the past two decades. However, unseen class label prediction is far less explored due to missing classes being invisible in training ML or DL models. In this work, we propose a fuzzy inference system to cope with such a challenge by adopting TSK+ fuzzy inference engine in conjunction with the Curvature-based Feature Selection (CFS) method. The practical feasibility of our system has been evaluated by predicting the positioning labels of networking devices within the realm of the Internet of Things (IoT). Competitive prediction performance confirms the efficiency and efficacy of our system, especially when a large number of continuous class labels are unseen during the model training stage.

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An intelligent human‐centric systems to diagnose breast cancer using machine learning and optimized feature selection techniques

Kumar,

Singh,

Mahadev

et al. 2023

Trans Emerging Tel Tech

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All over the world, breast cancer (BC) is the leading cause of cancer mortality among women. Computer‐aided methods can assist in early diagnosis. The proposed approach used SMOTE filter with Ch2 test techniques for class balance and feature section using eight different ML models Gaussian Naive Bayes (GNB), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM) with Linear and Radial Basis Function (RBF), Logistic Regression (LR), K‐nearest neighbor (KNN) and eXtreme Gradient Boosting (XGBoost). A Ch2 test determines the top five features—glucose, HOMA, resistin, BMI, and insulin. Metrics such as accuracy, precision, recall, and F1‐Score are used to compare the performance of models. More than 99% accuracy was achieved by the proposed XGBoost model. Compared to the other breast cancer prediction models, the proposed model had an average accuracy improvement of 9.30%. As a result of our proposed model, breast cancer diagnosis will be more efficient based on risk factors. The proposed prediction model can also predict various breast cancer features. In addition to improving diagnostic decision‐support systems, the proposed model should be able to predict breast cancer disease accurately.

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Curvature-based feature selection with application in classifying electronic health records

Cited by 15 publications

References 42 publications

Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

Seen to Unseen: When Fuzzy Inference System Predicts IoT Device Positioning Labels That Had Not Appeared in Training Phase

An intelligent human‐centric systems to diagnose breast cancer using machine learning and optimized feature selection techniques

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