Breast Cancer Identification from Patients’ Tweet Streaming Using Machine Learning Solution on Spark

Omran, Nahla F.; Ghany, Sara F. Abd-el; Saleh, Hager; Nabil, Ayman

doi:10.1155/2021/6653508

Cited by 8 publications

(3 citation statements)

References 31 publications

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“…In regular ML approach, five ML algorithms, such as decision tree (DT) [ 29 ], support vector machine (SVM) [ 30 ], K -nearest neighbor algorithm (KNN) [ 31 ], random forest (RF) [ 32 ], and naive Bayes (NB) [ 33 ] were used to compare with the optimized deep RNN. Grid search with cross-validation is used to optimize ML algorithms and improve ML algorithms performance.…”

Section: Methodsmentioning

confidence: 99%

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Predicting Breast Cancer Based on Optimized Deep Learning Approach

Saleh

ghany²,

Alyami

et al. 2022

Computational Intelligence and Neuroscience

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Breast cancer is a dangerous disease with a high morbidity and mortality rate. One of the most important aspects in breast cancer treatment is getting an accurate diagnosis. Machine-learning (ML) and deep learning techniques can help doctors in making diagnosis decisions. This paper proposed the optimized deep recurrent neural network (RNN) model based on RNN and the Keras–Tuner optimization technique for breast cancer diagnosis. The optimized deep RNN consists of the input layer, five hidden layers, five dropout layers, and the output layer. In each hidden layer, we optimized the number of neurons and rate values of the dropout layer. Three feature-selection methods have been used to select the most important features from the database. Five regular ML models, namely decision tree (DT), support vector machine (SVM), random forest (RF), naive Bayes (NB), and K-nearest neighbor algorithm (KNN) were compared with the optimized deep RNN. The regular ML models and the optimized deep RNN have been applied the selected features. The results showed that the optimized deep RNN with the selected features by univariate has achieved the highest performance for CV and the testing results compared to the other models.

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Section: Methodsmentioning

confidence: 99%

“…CV separates the data set into k subsets in order to train ML algorithms on k −1 subsets (the training set). The remainder is used to test ML algorithms [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

Predicting Breast Cancer Based on Optimized Deep Learning Approach

Saleh

ghany²,

Alyami

et al. 2022

Computational Intelligence and Neuroscience

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“…As a result of the research direction in content analysis, the research organizations start raising funding to provide novel solutions to combat COVID-19 in terms of analyzing the misleading information about the COVID-19 pandemic [ 5 – 10 ]. Recently, machine learning and deep learning are playing a vital role in different areas such as sentiment analysis [ 11 , 12 ]; Alzheimer detection [ 13 ]; prediction cancer [ 14 ], and others [ 15 , 16 ]. The researchers have utilized the collected datasets related to COVID-19 through social media to evaluate their proposed approaches [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Hybrid Deep Learning Model to Detect COVID‐19 Misleading Information

Alouffi

Alharbi

Sahal

et al. 2021

Computational Intelligence and Neuroscience

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Fake news is challenging to detect due to mixing accurate and inaccurate information from reliable and unreliable sources. Social media is a data source that is not trustworthy all the time, especially in the COVID-19 outbreak. During the COVID-19 epidemic, fake news is widely spread. The best way to deal with this is early detection. Accordingly, in this work, we have proposed a hybrid deep learning model that uses convolutional neural network (CNN) and long short-term memory (LSTM) to detect COVID-19 fake news. The proposed model consists of some layers: an embedding layer, a convolutional layer, a pooling layer, an LSTM layer, a flatten layer, a dense layer, and an output layer. For experimental results, three COVID-19 fake news datasets are used to evaluate six machine learning models, two deep learning models, and our proposed model. The machine learning models are DT, KNN, LR, RF, SVM, and NB, while the deep learning models are CNN and LSTM. Also, four matrices are used to validate the results: accuracy, precision, recall, and F1-measure. The conducted experiments show that the proposed model outperforms the six machine learning models and the two deep learning models. Consequently, the proposed system is capable of detecting the fake news of COVID-19 significantly.

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Applications of supervised machine learning techniques with the goal of medical analysis and prediction: A case study of breast cancer

Kumar

Pande

2022

Predictive Modeling in Biomedical Data Mining and Analysis

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Breast Cancer Identification from Patients’ Tweet Streaming Using Machine Learning Solution on Spark

Cited by 8 publications

References 31 publications

Predicting Breast Cancer Based on Optimized Deep Learning Approach

Predicting Breast Cancer Based on Optimized Deep Learning Approach

An Optimized Hybrid Deep Learning Model to Detect COVID‐19 Misleading Information

Applications of supervised machine learning techniques with the goal of medical analysis and prediction: A case study of breast cancer

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