Comparison of the performance of machine learning algorithms in breast cancer screening and detection: A protocol

@article{Salod2019ComparisonOT,
  title={Comparison of the performance of machine learning algorithms in breast cancer screening and detection: A protocol},
  author={Zakia Salod and Yashik Singh},
  journal={Journal of Public Health Research},
  year={2019},
  volume={8}
}
Background: Breast Cancer (BC) is a known global crisis. The World Health Organization reports a global 2.09 million incidences and 627,000 deaths in 2018 relating to BC. The traditional BC screening method in developed countries is mammography, whilst developing countries employ breast self-examination and clinical breast examination. The prominent gold standard for BC detection is triple assessment: i) clinical examination, ii) mammography and/or ultrasonography; and iii) Fine Needle Aspirate… 

Figures from this paper

Developing the breast cancer risk prediction system using hybrid machine learning algorithms

The developed predictive system can accurately identify persons who are at elevated risk for BC and can be used as an essential clinical screening tool for the early prevention of BC and serve as an important tool for developing preventive health strategies.

Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques, and Their Analysis

The comparative analysis of machine learning, deep learning and data mining techniques being used for the prediction of breast cancer is presented to find out the most appropriate method that will support the large dataset with good accuracy of prediction.

Data imputation in deep neural network to enhance breast cancer detection

A composite imputed multilayer fully connected deep networks (MFCN) that can replace the incomplete values in the dataset to augment the efficient feature learning for breast cancer classification is proposed.

Intelligent breast cancer diagnostic system empowered by deep extreme gradient descent optimization.

This endeavor aims to build a deep learning-based model for the prediction of breast cancer with a better accuracy and outperform many other approaches by attaining 98.73 % accuracy, 99.60% specificity, 98.43% sensitivity, and 99.48% precision.

SVM-ANN Optimized Algorithm for the Classification of Breast Cancer Data as Benign and Malignant

This research aims to establish a predictive model that could really identify breast cancer earlier on and increase survival rates and the proposed algorithm SVM-ANN optimized algorithm significantly outperformed other current algorithms.

Breast Cancer Classification using Deep Learned Features Boosted with Handcrafted Features

A novel framework for classification of breast cancer using mammograms is proposed that combines robust features extracted from novel Convolutional Neural Network features with handcrafted features including HOG (Histogram of Oriented Gradients) and LBP (Local Binary Pattern).

Review of Intelligent Algorithms for Breast Cancer Control: a Latin America Perspective

The Prevention stage of cancer control has not been addressed with intelligent algorithms, and the Early Detection stage has been very little addressed; private data sources could be beneficial in this type of research, but the difficulty in accessing them is a barrier for researchers.

A Comprehensive Review Study on: Optimized Data Mining, Machine Learning and Deep Learning Techniques for Breast Cancer Prediction in Big Data Context

The aim of this review article is to help to choose the appropriate breast cancer prediction techniques specifically in the Big data environment to produce effective and efficient result, because “Early detection is the key to prevention-in case of any cancer”.

Federated Learning through Goal Programming: a Computational Study in Cancer Detection

  • M. RepettoD. Torre
  • Computer Science
    2022 5th International Conference on Signal Processing and Information Security (ICSPIS)
  • 2022
This computational study investigates whether a privacy-compliant Federated Learning framework is suitable for cancer detection and tests the Federated Goal Programming framework in the context of cancer detection.

References

SHOWING 1-10 OF 24 REFERENCES

Performance Evaluation of Machine Learning Methods for Breast Cancer Prediction

Five different classification models including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Neural Network (NN) and Logistics Regression (LR) are used for the classification of two different datasets related to breast cancer.

Triple Assessment of Breast - Gold Standard in Mass Screening for Breast Cancer Diagnosis

Evaluation of accuracy of triple assessment in the early detection of breast carcinoma found it to be a very useful diagnostic tool to evaluate patients with breast lumps and to detect patients with Breast cancers with an overall accuracy of 99.3%.

A Data Mining Techniques for Diagnosis of Breast Cancer Disease

Ensemble methods used for improving classifier performance to determine whether patients have breast cancer or not and the results are evaluated using metrics: Sensitivity, specificity and classification accuracy.

A Novel Approach for Breast Cancer Detection Using Data Mining Techniques

In this experiment, three classification techniques in Weka software are compared and results show that Sequential Minimal Optimization (SMO) has higher prediction accuracy i.e. 96.2% than IBK and BF Tree methods.

Screening for cancer in low- and middle-income countries.

Cancer screening recommendations: an international comparison of high income countries

Recommendations have important commonalities for well-established cancer screening programs such as breast and cervical cancer, with greater variation between countries regarding prostate, colorectal, lung, and skin cancer screening.

The Risk of Determining Risk with Multivariable Models

The purpose in the current research was to note the frequency with which multivariable analyses now appear in general medical journals, to identify some common problems and desirable precautions in the analyses, and to determine how well the challenges are being met.

Greedy function approximation: A gradient boosting machine.

A general gradient descent boosting paradigm is developed for additive expansions based on any fitting criterion, and specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification.

A decision-theoretic generalization of on-line learning and an application to boosting

The model studied can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting, and it is shown that the multiplicative weight-update Littlestone?Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems.

XGBoost: A Scalable Tree Boosting System

This paper proposes a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning and provides insights on cache access patterns, data compression and sharding to build a scalable tree boosting system called XGBoost.