Applying Machine Learning Algorithms for Early Diagnosis and Prediction of Breast Cancer Risk

Shaikh, Tawseef Ayoub; Ali, Rashid

doi:10.1007/978-981-13-1217-5_57

Cited by 15 publications

(7 citation statements)

References 17 publications

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“…In recent years, several neuroimaging studies have utilized machine learning (ML) algorithms for detection and diagnosis of PD [33]- [35]. Various modalities like Magnetic Resonance Imaging (MRI), Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET) and functional Magnetic Resonance Imaging (fMRI) are used within these research to diagnose PD [36], [37].…”

Section: Related Workmentioning

confidence: 99%

Generalization Performance of Deep Learning Models in Neurodegenerative Disease Classification

Yağış

García

Citi

2019

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Over the past decade, machine learning gained considerable attention from the scientific community and has progressed rapidly as a result. Given its ability to detect subtle and complicated patterns, deep learning (DL) has been utilized widely in neuroimaging studies for medical data analysis and automated diagnostics with varying degrees of success. In this paper, we question the remarkable accuracies of the best performing models by assessing generalization performance of the stateof-the-art convolutional neural network (CNN) models on the classification of two most common neurodegenerative diseases, namely Alzheimer's Disease (AD) and Parkinson's Disease (PD) using MRI. We demonstrate the impact of the data division strategy on the model performances by comparing the results derived from two different split approaches. We first evaluated the performance of the CNN models by dividing the dataset at the subject level in which all of the MRI slices of a patient are put into either training or test set. We then observed that pooling together all slices prior to applying cross-validation, as erroneously done in a number of previous studies, leads to inflated accuracies by as much as 26% for the classification of the diseases.

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Section: Related Workmentioning

confidence: 99%

Generalization Performance of Deep Learning Models in Neurodegenerative Disease Classification

Yağış

García

Citi

2019

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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“…Figure 11 shows the graphical representation of different models implemented by authors on the same Wisconsin breast cancer dataset with their achieved accuracy. Shaikh, T. A. et al [37] used Weka's WrapperSubsetEval dimensionality reduction algorithm on the Wisconsin breast cancer dataset to reduce the dataset's size. It was found that the naïve Bayes, J48, k-NN, and SVM models had raised their accuracy from 97.91% in the case of the Wisconsin dataset to 99.97% in the closing tests.…”

Section: Comparison Of Osel Model With Existing Modelsmentioning

confidence: 99%

Optimized Stacking Ensemble Learning Model for Breast Cancer Detection and Classification Using Machine Learning

et al. 2022

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Breast cancer is the most frequently encountered medical hazard for women in their forties, affecting one in every eight women. It is the greatest cause of death worldwide, and early detection and diagnosis of the disease are extremely challenging. Breast cancer currently exceeds all other female cancers, including ovarian cancer. Researchers can use access to healthcare records to find previously unknown healthcare trends. According to the National Cancer Institute (NCI), breast cancer mortality rates can be lowered if the disease is detected early. The novelty of our work is to develop an optimized stacking ensemble learning (OSEL) model capable of early breast cancer prediction. A dataset from the University of California, Irvine repository was used, and comparisons to modern classifier models were undertaken. The implementation analyses reveal the unique approach’s efficacy and superiority when compared to existing contemporary categorization models (AdaBoostM1, gradient boosting, stochastic gradient boosting, CatBoost, and XGBoost). In every classification task, predictive models may be used to predict the class level, and the current research explores a range of predictive models. It is better to integrate multiple classification algorithms to generate a set of prediction models capable of predicting each class level with 91–99% accuracy. On the breast cancer Wisconsin dataset, the suggested OSEL model attained a maximum accuracy of 99.45%, much higher than any single classifier. Thus, the study helps healthcare professionals find breast cancer and prevent it from happening.

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“…Using four data mining classifiers SVM, J48, k-NN and Naive Bayes, Shaikh, Tawseef Ayoub, and Rashid Ali [23] performed an experiment on two well-known breast cancer datasets, Wisconsin and Portuguese. They have used WEKA tool for the conduction of an experiment and finally, they applied MATLAB and Weka for showing their results.…”

Section: Related Workmentioning

confidence: 99%

Stacking-Based Ensemble Framework and Feature Selection Technique for the Detection of Breast Cancer

Chaurasia

Pal

2021

SN COMPUT. SCI.

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Breast cancer is the second most common cancer in women worldwide. The uncontrolled growth of breast cells is called breast cancer. The treatment of human breast cancer is a very critical process, and sometimes certain indicators may produce negative results. To avoid this misleading outcome situation, a reliable and accurate breast cancer diagnosis system must be available. The machine learning (ML) method is a modern and accurate technique that researchers have recently applied to predict and diagnose breast cancer. In this research article, we developed stack-based ensemble techniques and feature selection methods for the comprehensive performance of the algorithm and comparative analysis of breast cancer datasets with reduced attributes and all attributes. In this article, we first take the SVM, k nearest neighbors, Naive Bayes and perceptron as four ML algorithms as sub-models that have been trained and predicted from, and then combine them to make a new model called blending (stacking). Finally, logistic regression is used to predict the stacked model. It is significant that sub-models produce different results that are not correlated predictions. The stacking technique is best when all the submodels are skillfully combined together. This article uses the five-feature selection technique because it affects the overall performance of the model. Unrelated or moderately related features may adversely affect the behavior of the model. After applying the feature selection method, now we have data set with reduced features as well as all features. We implemented logistic regression on a dataset with all features and a dataset with reduced features. Finally, we see that the dataset with reduced features has got improved accuracy.

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Applying Machine Learning Algorithms for Early Diagnosis and Prediction of Breast Cancer Risk

Cited by 15 publications

References 17 publications

Generalization Performance of Deep Learning Models in Neurodegenerative Disease Classification

Generalization Performance of Deep Learning Models in Neurodegenerative Disease Classification

Optimized Stacking Ensemble Learning Model for Breast Cancer Detection and Classification Using Machine Learning

Stacking-Based Ensemble Framework and Feature Selection Technique for the Detection of Breast Cancer

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