Application of Machine Learning Approaches in Health Care Sector to The Diagnosis of Breast Cancer

Wassim, Ahrou; Elarbi, Elalaouy; Khadija, Rhoulami

doi:10.1088/1742-6596/2224/1/012012

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…They observed that a 96.1% accuracy could be attained using the decision tree approach. Six data mining methods for detecting breast cancer were evaluated by Wassim et al [27]. Decision trees, naive Bayes classifiers, support vector machines, random forests, k-nearest neighbors, and neural networks were some of the algorithms employed.…”

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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“…Research on Badr and Abou uses python with genetic algorithm and cuckoo search algorithm with 84% accuracy [8] [16]. Wassim's research uses a rapidminer with the K Nearest Neighbors (K-NN) algorithm, Naive Bayes (NB), and the Support Vector Machine (SVM) with the best accuracy owned by SVM with a size of 97.07% [17].…”

Section: Introductionmentioning

confidence: 99%

Classification Algorithm Analysis for Breast Cancer

et al. 2023

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Breast cancer in women is a type of disease that is the main cause of death in women according to world breast cancer data. Therefore, early detection of breasts is needed significantly to improve life. If a woman has been identified, then rehabilitation and treatment on an incentive basis are needed to reduce the worse. This study used a dataset collected by the University of Wisconsin Hospitals, Madison (https://atapdata.ai/). This research conducted experiments using several data mining classification strategies to predict breast cancer using machine learning algorithms. The Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes, Random Forest, Decision Tree, Deep Learning (H2O), and Neural Network are used to classify algorithms. From these algorithms’ classification, we compare accuracy, best classification, and compare algorithm performance with curve ROC (RapidMiner Studio Core) to see which performance algorithm has the best quality for classification. From the analysis results, the deep learning algorithm with Tanh and Exprectifier activation function has a good accuracy of 93.14%, and the best classification with 89.62%. In addition, deep learning has found the best quality from the ROC curve results on the dataset used in this research.

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Application of Machine Learning Approaches in Health Care Sector to The Diagnosis of Breast Cancer

Cited by 2 publications

References 15 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

Classification Algorithm Analysis for Breast Cancer

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