Feature Selection with Fast Correlation-Based Filter for Breast Cancer Prediction and Classification Using Machine Learning Algorithms

Khourdifi, Youness; Bahaj, Mohamed

doi:10.1109/isaect.2018.8618688

Cited by 21 publications

(8 citation statements)

References 8 publications

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“…According to (Khourdifi & Bahaj, 2018) and (Jain, Jain, & Jain, 2018), the Fast Correlation-Based Filter (FCBF) algorithm consists of two steps: the first one is a relevance analysis, aimed at classifying the input variables according to a relevance score, calculated as a symmetric uncertainty with respect to the output target. This step is also used to ignore irrelevant variables, which are those whose ranking score is below a predefined threshold.…”

Section: A Chi-square (X²)mentioning

confidence: 99%

A Machine Learning Based Approach to Enhance Mooc Users’ Classification

Mourdi¹,

Sadgal²,

Fathi³

et al. 2020

Turkish Online Journal of Distance Education

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At the beginning of the 2010 decade, the world of education and more specifically e-learning was revolutionized by the emergence of Massive Open Online Courses, better known by their acronym MOOC. Proposed more and more by universities and training centers around the world, MOOCs have become an undeniable asset for any student or person seeking to complete their initial training with free distance courses open to all areas. Despite the remarkable number of course enrollees, MOOCs have a huge dropout rate of up to 90%. This rate significantly affects the efforts made by the moderators for the success of this pedagogical model and negatively influences the learners' experience and their supervision. To address this problem and help instructors streamline their interventions, we present a solution to classify MOOC learners into three distinct classes. The approach proposed in this paper is based on the filters methods to select the most relevant attributes and ensembling methods of machine learning algorithms. This approach has been validated by four MOOC courses from Stanford University. In order to prove the performance of the model (92.2%), a comparative study between the proposed model and other algorithms was made on several performance measures.

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Section: A Chi-square (X²)mentioning

confidence: 99%

A Machine Learning Based Approach to Enhance Mooc Users’ Classification

Mourdi¹,

Sadgal²,

Fathi³

et al. 2020

Turkish Online Journal of Distance Education

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“…In paper [27], the author used different types of classifiers along with different types of biomarkers. Paper [21], the author has done a comparative study, which includes the different types of classifiers and predicted that the SVM without the fast co-relation based filter is providing highest accuracy which is97.9 percent. In paper [23], the author for classification purposes performs Logistic regression.…”

Section: Rfsvm: a Novel Classification Technique For Breast Cancer DImentioning

confidence: 99%

RFSVM: A Novel Classification Technique for Breast Cancer Diagnosis

Soni*¹,

Bora²,

Ghosh³

et al. 2019

IJITEE

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Cancer is a disease, which develops, in human body due to gene mutation. Due to various factor cells turn into cancerous cell and grow rapidly while damaging normal cells. Many women get affected by breast cancer, which might even cause death if not treated at early stage. Early detection of breast cancer is highly important to increase the survival rate. Machine learning methods and technologies are making it possible to classify and detect the class in an accurate manner. Among other classifiers, random forest and support vector machine are two classifiers that have a good classification power. In this, research a combination of these two classifier i.e. Random Forest and Support Vector Machine (RFSVM) is proposed for early diagnosis of breast cancer cell using Wisconsin Breast Cancer Dataset (WBCD). Using different train-test data ratio experiments are performed and an average of more than 98percentage accuracy is achieved using this hybrid classifier. This paper overcomes the over-fitting problem of random forest and the need of tuning the parameters of Support Vector Machine. Even with limited data available, the classifier tunes its parameters so well to give a highly accurate result.

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“…However, it is barely essential to categorize them to automate and speed up disease diagnosis. As per the report of American Cancer Society [3], the disease breast cancer affects more women than any other cancer. Overgrowth of the cells lining the breast ducts is the most common cause of breast tumors, which might be either benign or malignant [4].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Ensemble Method Using K-Fold Cross Validation for the Early Detection of Benign and Malignant Breast Cancer

Mahesh¹,

Kaladevi²,

Balajee³

et al. 2022

IJIE

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In comparison to all other malignancies, breast cancer is the most commonform of cancer, among women. Breast cancer prediction has been studied by a number of researchers, and is considered as a serious threat to women. Clinicians are finding it difficult to create a treatment approach that will help patients live longer, due to the lack of solid predictive models which predicts the outcome in early stages by analyzing history of patient’s data. Rates of this malignancy have been observed to rise, more with industrialization and urbanization, as well as with early detection facilities. It is still considerably more prevalent in very developed countries, but it is rapidly spreading to developing countries as well. The purpose of this work is to offer a report on the disease of breast cancer in which we used available technical breakthroughs to construct breast cancer survivability prediction models. The Machine Learning (ML) techniques, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT) Classifier, Random Forests (RF), and Logistic Regression (LR)are used as base Learners and their performance has been compared with the ensemble method, eXtreme Gradient Boosting(XGBoost). For performance comparison, we employed k-fold cross validation method to measure the unbiased estimate of these prediction models. The results indicated that XGBoost outperformed with an accuracy of 97.81% compared to other ML algorithms.

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Feature Selection with Fast Correlation-Based Filter for Breast Cancer Prediction and Classification Using Machine Learning Algorithms

Cited by 21 publications

References 8 publications

A Machine Learning Based Approach to Enhance Mooc Users’ Classification

A Machine Learning Based Approach to Enhance Mooc Users’ Classification

RFSVM: A Novel Classification Technique for Breast Cancer Diagnosis

An Efficient Ensemble Method Using K-Fold Cross Validation for the Early Detection of Benign and Malignant Breast Cancer

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