Using Feature Selection Techniques to Improve the Accuracy of Breast Cancer Classification

Saoud, Hajar; Ghadi, Abderrahim; Ghailani, Mohamed; Abdelhakim, Boudhir Anouar

doi:10.1007/978-3-030-11196-0_28

Cited by 18 publications

(6 citation statements)

References 4 publications

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“…And missing values was handled by using K-Nearest Neighbor imputation technique. Feature selection is carried out while executing the classification algorithm [12] Extra trees and Random forest classifiers From the previous table we found that the resulted most important features of the 2 techniques are close , except the order of quadrant_syptoms and g.…”

Section: Data Preprocessingmentioning

confidence: 99%

Overcome Challenges in Primary Datasets: Practical Exploration and Preprocessing of Breast Cancer Dataset by Applying Machine Learning Algorithms

Gharib

Labib

Rostom

et al. 2023

مجلة الجمعیة المصریة لنظم المعلومات وتکنولوجیا الحاسبات

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In 2020 Cancer caused 685,000 deaths worldwide, thus it is considered the second lethal disease globally. Because diagnosis of Metastatic Breast Cancer (MBC) patients is challenging, a prediction tool is needed during the diagnosis stage to define and prioritize patients who are more likely to develop metastasis and provide them with optimal palliative or supportive care. Machine Learning (ML) as a subset of Artificial Intelligence (AI) has been applied in oncology for early detection of cancer, identifying patients with high risk of survival, cancer morbidity, and mortality rate, besides predicting drug response. . One of the main applications of Machine Learning in public health is the identification and prediction of populations with high risk for developing specific adverse health outcomes, and development of appropriate targeted health interventions. Better data quality is crucial for better patient targeting and Informed Decision-Making. Also, the more and sufficient quality data the better machine learning model performance. Noisy or unclean may lead to inaccurate or faulty prediction, which is crucial in medical field. Consequently, data quality is essential for better Machine Learning model performance. The aim of this research is to determine the key challenges of using raw datasets, and illustrates how Machine Learning techniques can be used to explore and preprocess the dataset to overcome these challenges.

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Section: Data Preprocessingmentioning

confidence: 99%

Overcome Challenges in Primary Datasets: Practical Exploration and Preprocessing of Breast Cancer Dataset by Applying Machine Learning Algorithms

Gharib

Labib

Rostom

et al. 2023

مجلة الجمعیة المصریة لنظم المعلومات وتکنولوجیا الحاسبات

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“…A similar study by Saoud et al [21] utilized the most effective first search approach for feature selection and wrapper models, such as artificial neural networks (ANN), Bayesian networks, SVM, K-NN, Decision Trees, and Logistic Regression (LR). Different numbers of features were chosen by each model from the BCW dataset.…”

Section: Related Workmentioning

confidence: 99%

Integration of ML Techniques for Early Detection of Breast Cancer: Dimensionality Reduction Approach

Hanon,

Salman

2024

ISI

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Nowadays, the diagnosis of breast cancer (DBC) helps doctors make early detection of breast cancer into non-cancerous (benign B) and cancerous (malignant M). Therefore, using machine learning (ML) algorithms is a solution to diagnosing and predicting symptoms related to DBC. The increased computational complexity, data size, overfitting, and longer training times harm early diagnosis accuracy. In this paper, propose a dimensionality reduction model integrating PCA and KNN for early breast cancer detection. which is used to diagnose and predict breast cancer (DPBC) based on reduced data size by selecting the best features that capture most of the variance in the data. The performance of the proposed model is evaluated with indices such as accuracy, precision, and f-score. Results for the DPBC model were obtained by using the Breast Cancer Wisconsin medical datasets (BCW).

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“…In data pre-processing, data is normalized [32]. Data pre-processing is performed to identify missing values and remove the missing values from the dataset.…”

Section: 1data Pre-processingmentioning

confidence: 99%

Machine learning techniques with ANOVA for the prediction of breast cancer

2022

IJATEE

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Breast cancer is one of the most common cancer among females. In this paper, machine learning techniques are applied to molecular taxonomy of breast cancer international consortium (METABRIC) dataset to extract prime clinical attributes to get high accuracy. Analysis of variance (ANOVA), the statistical method, is used for clinical feature selection. Five different machine learning algorithms are implemented, which are support vector machine (SVM), decision tree, random forest, AdaBoost and artificial neural network (ANN). Among all the machine learning classifiers, ANN gives the highest accuracy of 87.43%. This statistical technique is helpful for the detection of breast cancer, and it will increase the survival rate of females.

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Using Feature Selection Techniques to Improve the Accuracy of Breast Cancer Classification

Cited by 18 publications

References 4 publications

Overcome Challenges in Primary Datasets: Practical Exploration and Preprocessing of Breast Cancer Dataset by Applying Machine Learning Algorithms

Overcome Challenges in Primary Datasets: Practical Exploration and Preprocessing of Breast Cancer Dataset by Applying Machine Learning Algorithms

Integration of ML Techniques for Early Detection of Breast Cancer: Dimensionality Reduction Approach

Machine learning techniques with ANOVA for the prediction of breast cancer

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