Efficient Role of Machine Learning Classifiers in the Prediction and Detection of Breast Cancer

2020 ◽  
Author(s):  
Bibhuprasad Sahu ◽  
Amrutanshu Panigrahi
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

scholarly journals Prediction of survival and metastasis in breast cancer patients using machine learning classifiers

2019 ◽  
Vol 7 (3) ◽  
pp. 293-299 ◽  
Author(s):  
Leili Tapak ◽  
Nasrin Shirmohammadi-Khorram ◽  
Payam Amini ◽  
Behnaz Alafchi ◽  
Omid Hamidi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Patients ◽  
Breast Cancer Patients ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Prediction Of Survival

scholarly journals Machine Learning Classifiers on Breast Cancer Recurrences

2021 ◽  
Vol 192 ◽  
pp. 2742-2752
Author(s):  
Vincent Peter C. Magboo ◽  
Ma. Sheila A. Magboo
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

An Analysis of Machine Learning Classifiers in Breast Cancer Diagnosis

2019 ◽  
Author(s):  
Fabiano Teixeira ◽  
Joao Luis Zeni Montenegro ◽  
Cristiano Andre da Costa ◽  
Rodrigo da Rosa Righi
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

Discovering Mammography-based Machine Learning Classifiers for Breast Cancer Diagnosis

2011 ◽  
Vol 36 (4) ◽  
pp. 2259-2269 ◽  
Author(s):  
Raúl Ramos-Pollán ◽  
Miguel Angel Guevara-López ◽  
Cesar Suárez-Ortega ◽  
Guillermo Díaz-Herrero ◽  
Jose Miguel Franco-Valiente ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

Role of Feature Engineering and Classifier Selection for Machine Learning Predictions

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
Makarand Velankar ◽  
Vaibhav Khatavkar ◽  
Vinayak Jagtap ◽  
Parag Kulkarni
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Prediction Accuracy ◽  
Feature Engineering ◽  
Machine Learning Classifiers ◽  
Classifier Selection ◽  
Learning Classifiers ◽  
Proposed Model

Features play a crucial role in several computational tasks. Feature values are input to machine learning algorithms for the prediction. The prediction accuracy depends on various factors such as selection of dataset, features and machine learning classifiers. Various feature selection and reduction approaches are experimented with to obtain better accuracies and reduce the computational overheads. Feature engineering is designing new features suitable for a specific task with the help of domain knowledge. The challenges in feature engineering are presented for the computational music domain as a case study. The experiments are performed with different combinations of feature sets and machine learning classifiers to test the accuracy of the proposed model. Music emotion recognition is used as a case study for the experimentation. Experimental results for the task of music emotion recognition provide insights into the role of features and classifiers in prediction accuracy. Different machine learning classifiers provided varied results, and the choice of a classifier is also an important decision to be made in the proposed model. The engineered features designed with the help of domain experts improved the results. It emphasizes the need for feature engineering for different domains for prediction accuracy improvement. Approaches to design an optimized model with the appropriate feature set and classifier for machine learning tasks are presented.

THE EFFECT OF DATASETS ON BREAST CANCER DETECTION MODELS

2021 ◽  
Vol 4 (4) ◽  
pp. 309-315
Author(s):  
Kumawuese Jennifer Kurugh ◽  
Muhammad Aminu Ahmad ◽  
Awwal Ahmad Babajo
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Learning Algorithms ◽  
Cancer Classification ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
Machine Learning Classifiers ◽  
Breast Cancer Classification ◽  
Learning Classifiers

Datasets are a major requirement in the development of breast cancer classification/detection models using machine learning algorithms. These models can provide an effective, accurate and less expensive diagnosis method and reduce life losses. However, using the same machine learning algorithms on different datasets yields different results. This research developed several machine learning models for breast cancer classification/detection using Random forest, support vector machine, K Nearest Neighbors, Gaussian Naïve Bayes, Perceptron and Logistic regression. Three widely used test data sets were used; Wisconsin Breast Cancer (WBC) Original, Wisconsin Diagnostic Breast Cancer (WDBC) and Wisconsin Prognostic Breast Cancer (WPBC). The results show that datasets affect the performance of machine learning classifiers. Also, the machine learning classifiers have different performances with a given breast cancer dataset

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