Hybrid Feature Selection and Bayesian Optimization with Machine Learning for Breast Cancer Prediction

Mate, Yash; Somai, Neelam

doi:10.1109/icaccs51430.2021.9441914

Cited by 9 publications

(5 citation statements)

References 7 publications

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“…On the WDBC dataset, the results demonstrated an accuracy of 97.18%. Mate and Somai (2021) introduced the Bayesian optimization technique, hyper-parameter tunings, and feature selection techniques (e.g., Pearson's coefficient, chisquare test, logistic regression, and random forest). They obtained an accuracy of 96.2% with the extra tree classifier algorithm on the WDBC dataset.…”

Section: Diagnosis With Fs Methodsmentioning

confidence: 99%

“…ML greatly assists in saving operational costs and improving the speed of the data analysis (Russell & Norvig, 2021). For example, in recent years, with the increase in community knowledge and AI development, especially in data mining, many studies have been conducted regarding the early detection of breast cancer (Mate & Somai, 2021). On the one hand, classification models exploited in data-mining methods determine the type of tumor and the accuracy of diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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Two new feature selection methods based on learn-heuristic techniques for breast cancer prediction: a comprehensive analysis

2022

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In recent decades, breast cancer has become one of the leading causes of mortality among women. This disease is not preventable because of its unknown causes; however, its early diagnosis increases patients' recovery chances. Machine learning (ML) can be utilized to improve treatment outcomes in healthcare operations while diminishing costs and time. In this research, we suggest two novel feature selection (FS) methods based upon an imperialist competitive algorithm (ICA) and a bat algorithm (BA) and their combination with ML algorithms. This study aims to enhance diagnostic models' efficiency and present a comprehensive analysis to help clinical physicians make more precise and reliable decisions. K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), Naive Bayes, AdaBoost (AB), linear discriminant analysis (LDA), random forest (RF), logistic regression (LR), and artificial neural network (ANN) are some of the methods employed. Sensitivity, accuracy, precision, mean absolute error F-score, root mean square error, Kappa, and relative absolute error calculated the performance of the methods. This paper applied a distinctive integration of evaluation measures and ML algorithms using the wrapper feature selection based on ICA (WFSIC) and BA (WFSB) separately. We compared two proposed approaches for the performance of the classifiers. Also, we compared our best diagnostic model with previous works reported in the literature survey. Experimentations were performed on the Wisconsin diagnostic breast cancer (WDBC) dataset. Results reveal that the proposed framework that uses the BA with an accuracy of 99.12% surpasses the framework using the ICA and most previous works. Additionally, the RF classifier in the approach of FS based on BA emerges as the best model and outperforms others regarding its criteria. Besides, the results illustrate the role of our techniques in reducing the dataset dimensions up to 90% and increasing the performance of diagnostic models by over 99%. Moreover, the result demonstrates that there are more critical features than the optimum dataset obtained by proposed FS approaches that have been selected by most ML models, including the standard error of area, concavity, smoothness, perimeter, the worst of texture, compactness, radius, symmetry, smoothness, concavity, and the mean of concave points, fractal dimension, compactness, concavity that B Ali Ghodratnama

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Section: Diagnosis With Fs Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Two new feature selection methods based on learn-heuristic techniques for breast cancer prediction: a comprehensive analysis

2022

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“…From the wrapper category, we employed a recursive feature elimination (RFE) based on logistic regression [28] and an RFE based on support vector machine [29] techniques, respectively. Furthermore, from the embedded category, a LightGBM [30] and a random forest technique [31] were applied. To calculate the importance of a feature for each category, the scores of the associated FS techniques were used as input to the Fuzzy Inference System (FIS) 1 that was implemented with Mamdani inference methodology [32].…”

Section: Proposed Fs Methodologymentioning

confidence: 99%

Explainable Machine Learning for Knee Osteoarthritis Diagnosis Based on a Novel Fuzzy Feature Selection Methodology

Kokkotis

Ntakolia

Moustakidis³

et al. 2021

Preprint

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Knee Osteoarthritis (ΚΟΑ) is a degenerative joint disease of the knee that results from the progressive loss of cartilage. Due to KOA’s multifactorial nature and the poor understanding of its pathophysiology, there is a need for reliable tools that will reduce diagnostic errors made by clinicians. The existence of public databases has facilitated the advent of advanced analytics in KOA research however the heterogeneity of the available data along with the observed high feature dimensionality make this diagnosis task difficult. The objective of the present study is to provide a robust Feature Selection (FS) methodology that could: (i) handle the multidimensional nature of the available datasets and (ii) alleviate the defectiveness of existing feature selection techniques towards the identification of important risk factors which contribute to KOA diagnosis. For this aim, we used multidisciplinary data obtained from the Osteoarthritis Initiative database for individuals without or with KOA. The proposed fuzzy ensemble feature selection methodology aggregates the results of several FS algorithms (filter, wrapper and embedded ones) based on fuzzy logic. The effectiveness of the proposed methodology was evaluated using an extensive experimental setup that involved multiple competing FS algorithms and several well-known ML models. A 73.55 % classification accuracy was achieved by the best performing model (Random Forest classifier) on a group of twenty-one selected risk factors. Explainability analysis was finally performed to quantify the impact of the selected features on the model’s output thus enhancing our understanding of the rationale behind the decision-making mechanism of the best model.

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“…Their proposed approach achieved an accuracy of 96.4% for WBCD and 97.9% for WPBC. Mate et al [30] presented a hybrid model that combined FS and BO with machine learning for BC prediction. Extra Tree Classifier algorithms was the best classification method with accuracy of 96,2%.…”

Section: Literature Surveymentioning

confidence: 99%

Breast Cancer Diagnosis Using Feature Selection Approaches and Bayesian Optimization

Akkur¹,

TURK²,

Eroğul³

2023

Computer Systems Science and Engineering

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Breast cancer seriously affects many women. If breast cancer is detected at an early stage, it may be cured. This paper proposes a novel classification model based improved machine learning algorithms for diagnosis of breast cancer at its initial stage. It has been used by combining feature selection and Bayesian optimization approaches to build improved machine learning models. Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Ensemble Learning and Decision Tree approaches were used as machine learning algorithms. All experiments were tested on two different datasets, which are Wisconsin Breast Cancer Dataset (WBCD) and Mammographic Breast Cancer Dataset (MBCD). Experiments were implemented to obtain the best classification process. Relief, Least Absolute Shrinkage and Selection Operator (LASSO) and Sequential Forward Selection were used to determine the most relevant features, respectively. The machine learning models were optimized with the help of Bayesian optimization approach to obtain optimal hyperparameter values. Experimental results showed the unified feature selection-hyperparameter optimization method improved the classification performance in all machine learning algorithms. Among the various experiments, LASSO-BO-SVM showed the highest accuracy, precision, recall and F1-score for two datasets (97.95%, 98.28%, 98.28%, 98.28% for MBCD and 98.95%, 97.17%, 100%, 98.56% for MBCD), yielding outperforming results compared to recent studies.

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Hybrid Feature Selection and Bayesian Optimization with Machine Learning for Breast Cancer Prediction

Cited by 9 publications

References 7 publications

Two new feature selection methods based on learn-heuristic techniques for breast cancer prediction: a comprehensive analysis

Two new feature selection methods based on learn-heuristic techniques for breast cancer prediction: a comprehensive analysis

Explainable Machine Learning for Knee Osteoarthritis Diagnosis Based on a Novel Fuzzy Feature Selection Methodology

Breast Cancer Diagnosis Using Feature Selection Approaches and Bayesian Optimization

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