Method Based on Data Mining Techniques for Breast Cancer Recurrence Analysis

Morales-Ortega, Roberto Cesar; German, Lozano-Bernal; Patricia, Ariza-Colpas Paola; Rodriguez, Eugenia Arrieta; Clementina, Ospino-Mendoza Elisa; Jose, Caicedo-Ortiz; Alberto, Piñeres-Melo Marlon; Enrique, Mendoza-Palechor Fabio; Margarita, Roca-Vides

doi:10.1007/978-3-030-53956-6_54

Cited by 9 publications

(6 citation statements)

References 24 publications

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“…The RF algorithm was determined to have the highest accuracy (97.1%). Breast cancer recurrence has been analyzed using a data mining method that was proposed by Cesar et al [26]. The authors implemented the decision tree, naive Bayes, and support vector machine data mining techniques on the Wisconsin Breast Cancer dataset.…”

Section: Related Workmentioning

confidence: 99%

Comparison of the Effectiveness of Various Classifiers for Breast Cancer Detection Using Data Mining Methods

Al-Qazzaz,

Mohammed,

Al-Qazzaz

et al. 2023

Applied Sciences

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Countless women and men worldwide have lost their lives to breast cancer (BC). Although researchers from around the world have proposed various diagnostic methods for detecting this disease, there is still room for improvement in the accuracy and efficiency with which they can be used. A novel approach has been proposed for the early detection of BC by applying data mining techniques to the levels of prolactin (P), testosterone (T), cortisol (C), and human chorionic gonadotropin (HCG) in the blood and saliva of 20 women with histologically confirmed BC, 20 benign subjects, and 20 age-matched control women. In the proposed method, blood and saliva were used to categorize the severity of the BC into normal, benign, and malignant cases. Ten statistical features were collected to identify the severity of the BC using three different classification schemes—a decision tree (DT), a support vector machine (SVM), and k-nearest neighbors (KNN) were evaluated. Moreover, dimensionality reduction techniques using factor analysis (FA) and t-stochastic neighbor embedding (t-SNE) have been computed to obtain the best hyperparameters. The model has been validated using the k-fold cross-validation method in the proposed approach. Metrics for gauging a model’s effectiveness were applied. Dimensionality reduction approaches for salivary biomarkers enhanced the results, particularly with the DT, thereby increasing the classification accuracy from 66.67% to 93.3% and 90%, respectively, by utilizing t-SNE and FA. Furthermore, dimensionality reduction strategies for blood biomarkers enhanced the results, particularly with the DT, thereby increasing the classification accuracy from 60% to 80% and 93.3%, respectively, by utilizing FA and t-SNE. These findings point to t-SNE as a potentially useful feature selection for aiding in the identification of patients with BC, as it consistently improves the discrimination of benign, malignant, and control healthy subjects, thereby promising to aid in the improvement of breast tumour early detection.

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Section: Related Workmentioning

confidence: 99%

Comparison of the Effectiveness of Various Classifiers for Breast Cancer Detection Using Data Mining Methods

Al-Qazzaz,

Mohammed,

Al-Qazzaz

et al. 2023

Applied Sciences

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“…The non-parametric lazy algorithm is k-nearest neighbor. The nearest neighbors are chosen based on the Euclidean distance between the x and y vectors as calculated in (5). The k-NN outcome varies depending on the value of K. A large value of K will result in class overlap, whereas a smaller value of K will result in faster computations [23].…”

Section: K-nearest Neighbor (K-nn)mentioning

confidence: 99%

“…Accurate breast cancer behavior prediction is critical because it supports clinicians in their decisionmaking process, allowing for more customized therapy for patients and higher recovery possibilities [4]. The underlying characteristics that contribute to early breast cancer recurrence remain a top research concern for doctors and data scientists alike [5]. A range of machine learning algorithms and statistical techniques have improved breast cancer diagnosis and prediction in many research studies on cancer recurrence [4], [5].…”

Section: Introductionmentioning

confidence: 99%

“…The underlying characteristics that contribute to early breast cancer recurrence remain a top research concern for doctors and data scientists alike [5]. A range of machine learning algorithms and statistical techniques have improved breast cancer diagnosis and prediction in many research studies on cancer recurrence [4], [5]. These algorithms represented try two situations, the first was machine learning methods k-nearest neighbor (k-NN), and artificial neural network (ANN) [6], and the second is deep learning models like convolutional neural network (CNN), and recurrent neural networks (RNN) [7].…”

Section: Introductionmentioning

confidence: 99%

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Breast cancer recognition based on performance evaluation of machine learning algorithms

Salih

Ali

2022

IJEECS

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Breast cancer is the one common cause of death in both developed worlds and the most death-causing disease diagnosed among women. <span lang="EN-US">Early recognition of this condition can help to minimize death rates. The breast problem statement, in brief, is not reliable for accuracy recognition. They have a high degree of classification accuracy as well as diagnostic capabilities. The most common classifications are normal, benign cancer, and malignant cancer. Machine learning (ML) techniques are now widely used in the classification of breast cancer. In this paper, some machine learning technics have been investigated to diagnose breast cancer (BC) on magnetic resonance imaging (MRI) images using multi-step processes. The first step has been to take the MRI image as an input image and have been pre-processing an image, then use feature extraction by using (scale-invariant feature transform (SIFT), histogram of oriented gradient (HOG), local binary patterns (LBP), bag of words (BoW), and edge-oriented histogram (EOH)). Next step we implement the classifying algorithms (KNN, decision tree (DT), naïve Bayes, ANN, SVM, RF, AdaBoost), have been used to detect and classify the normal or breast cancer region for this purpose datasets like ACRIN-Contralateral-Breast-MRI, In and breast cancer MRI dataset) has been collected our breast cancer MRI images from Erbil and Sulaymaniyah hospital the results was 91.9%, the result of ACRIN was 97% and the results Breast Cancer was 92.3%.</span>

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“…R. Kate and R. Nadig (2016) [5] mentioned that physicians and healthcare workers may make more informed decisions regarding a patient's condition if breast cancer survivability can be accurately predicted. In the last decade, numerous data mining tools have been used to determine the factors affecting the survival of patients with breast cancer [6][7][8][9][10][11][12]. Due to the advancement of technology, many machine learning tools were used to predict and diagnose patients with breast cancer [13].…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Learning to Construct Breast Cancer Diagnosis Model

et al. 2022

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(1) Background: According to Taiwan’s ministry of health statistics, the rate of breast cancer in women is increasing annually. Each year, more than 10,000 women suffer from breast cancer, and over 2000 die of the disease. The mortality rate is annually increasing, but if breast cancer tumors are detected earlier, and appropriate treatment is provided immediately, the survival rate of patients will increase enormously. (2) Methods: This research aimed to develop a stepwise breast cancer model architecture to improve diagnostic accuracy and reduce the misdiagnosis rate of breast cancer. In the first stage, a breast cancer risk factor dataset was utilized. After pre-processing, Artificial Neural Network (ANN) and the support vector machine (SVM) were applied to the dataset to classify breast cancer tumors and compare their performances. The ANN achieved 76.6% classification accuracy, and the SVM using radial functions achieved the best classification accuracy of 91.6%. Therefore, SVM was utilized in the determination of results concerning the relevant breast cancer risk factors. In the second stage, we trained AlexNet, ResNet101, and InceptionV3 networks using transfer learning. The networks were studied using Adaptive Moment Estimation (ADAM) and Stochastic Gradient Descent with Momentum (SGDM) based optimization algorithm to diagnose benign and malignant tumors, and the results were evaluated; (3) Results: According to the results, AlexNet obtained 81.16%, ResNet101 85.51%, and InceptionV3 achieved a remarkable accuracy of 91.3%. The results of the three models were utilized in establishing a voting combination, and the soft-voting method was applied to average the prediction result for which a test accuracy of 94.20% was obtained; (4) Conclusions: Despite the small number of images in this study, the accuracy is higher compared to other literature. The proposed method has demonstrated the need for an additional productive tool in clinical settings when radiologists are evaluating mammography images of patients.

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Method Based on Data Mining Techniques for Breast Cancer Recurrence Analysis

Cited by 9 publications

References 24 publications

Comparison of the Effectiveness of Various Classifiers for Breast Cancer Detection Using Data Mining Methods

Comparison of the Effectiveness of Various Classifiers for Breast Cancer Detection Using Data Mining Methods

Breast cancer recognition based on performance evaluation of machine learning algorithms

Application of Deep Learning to Construct Breast Cancer Diagnosis Model

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