Clinical Text Classification with Word Representation Features and Machine Learning Algorithms

Almazaydeh, Laiali; Abuhelaleh, Mohammad; Tawil, Arar Al; Elleithy, Khaled

doi:10.3991/ijoe.v19i04.36099

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…F-score is a single number that summarizes how well a system or model performs in making accurate positive predictions and finding all positive cases. It combines two key metrics: precision (the accuracy of positive predictions) and recall (the ability to find all positive cases) [25]. The F1-score strikes a balance between these two factors, providing a single measure of performance.…”

Section: The F-scorementioning

confidence: 99%

A comparative analysis of convolutional neural networks for breast cancer prediction

Al Tawil,

Shaban,

Almazaydeh

2024

IJECE

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Breast cancer continues to be a substantial worldwide health concern, affecting millions of individuals each year; this emphasizes the critical nature of early detection in order to enhance patient prognoses. The present study aims to assess the classification performance of three convolutional neural network (CNN) architectures-visual geometry group 19 (VGG19), AlexNet, and residual network 50 (ResNet50)-with respect to breast cancer detection in medical images. Thorough assessments, encompassing metrics such as accuracy, precision, recall, and F-score, were undertaken to evaluate the diagnostic performance of the models. ResNet50 consistently outperforms other models, as evidenced by its highest accuracy and F-score. The research highlights the significant importance of carefully choosing suitable architectures for medical image analysis, with a specific focus on the detection of breast cancer. In addition, it demonstrates the capacity of deep learning models, such as ResNet50, to improve the diagnosis of breast cancer with exceptional precision and sensitivity, which is critical for reducing the occurrence of false positives and negatives in clinical environments. In addition, computational efficiency is taken into account; AlexNet is recognized as the most efficient model, which is advantageous in environments with limited resources. This study advances medical image processing by demonstrating the potential of CNNs in the detection of breast cancer. The results of this study establish a fundamental basis for sub- sequent inquiries and suggest approaches to improve timely detection and treatment, which will ultimately be advantageous for both patients and healthcare professionals.

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Section: The F-scorementioning

confidence: 99%

A comparative analysis of convolutional neural networks for breast cancer prediction

Al Tawil,

Shaban,

Almazaydeh

2024

IJECE

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show abstract

“…Text classification methods based on machine learning have become a research hotspot. Text classification methods based on machine learning [6] are mainly divided into two categories: traditional machine learning methods and deep learning methods.…”

Section: Related Workmentioning

confidence: 99%

“…Precision represents the proportion of the predicted actual positive samples in the predicted positive samples. The calculation method is shown in (6).…”

Section: Precisionmentioning

confidence: 99%

CNN-Bi-LSTM Model for MOOC Forum Post Classification

Zhang,

Sun

2023

Int. J. Emerg. Technol. Learn.

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The discussion forum of the massive open online course (MOOC) is a platform for students to communicate with teachers, teaching assistants, and platform managers. It is one of the important factors related to course quality. A reasonable classification of discussion posts in the forum will help students better communicate and solve problems, so as to improve the quality of teaching. Aiming at the classification of discussion forum posts, this paper proposes a text classification model integrating convolutional neural networks (CNN) and bidirectional long-short-term memory (Bi-LSTM). Firstly, the user types and behavior characteristics are analyzed to build the taxonomy. The taxonomy includes three categories: course related, teacher related and platform related. Then, a text classification model is constructed based on CNN and Bi-LSTM. In order to verify the effectiveness of the proposed model, it is applied to the classification of 19285 discussion posts from the MOOC platform of icourse163.org. The overall classification accuracy of the proposed model is 93.6%, which is 12%, 10%, and 8% higher than traditional machine learning methods, CNN and Bi-LSTM, respectively. The model is used for automatic text classification in MOOC discussion forum, which can provide effective help and support for learners, teachers and platform managers, and improve the automation level of MOOC platform.

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An Automated EEG Signal Analysis Using Discrete Wavelet Transform and Advanced Machine Learning Models

Almazaydeh,

Al Tawil,

Elleithy

et al. 2024

Smart Innovation, Systems and Technologies

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Clinical Text Classification with Word Representation Features and Machine Learning Algorithms

Cited by 5 publications

References 26 publications

A comparative analysis of convolutional neural networks for breast cancer prediction

A comparative analysis of convolutional neural networks for breast cancer prediction

CNN-Bi-LSTM Model for MOOC Forum Post Classification

An Automated EEG Signal Analysis Using Discrete Wavelet Transform and Advanced Machine Learning Models

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