Automatic extraction of knowledge for diagnosing COVID-19 disease based on text mining techniques: A systematic review

Mahdi, Amir Yasseen; Yuhaniz, Siti Sophiayati

doi:10.21533/pen.v9i2.1945

Cited by 2 publications

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“…Using the "sklearn" Python module, the following classifiers were applied to each training set: random forest (RF), logistic regression (LR), and support vector machines (SVM). Each algorithm is thoroughly described in in [53][54][55].…”

Section: Covid-19 Patients Categorization Based Ibssa-fsmentioning

confidence: 99%

An Intelligent Feature Selection Approach Based on a Novel Improve Binary Sparrow Search Algorithm for COVID-19 Classification

2023

IJIES

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This paper proposes an improved binary sparrow search algorithm (IBSSA) as a search strategy within the feature selection (FS) methods. Its main objective is to use clinical texts to improve COVID-19 patient categorization. The constant need for an efficient FS system and the favorable outcomes of swarming behavior in numerous optimization situations drove our efforts to develop a novel FS strategy. Additionally, clinical text data are frequently highly dimensional and contain uninformative features, which have a major impact on the classifier's accuracy, making FS a key machine-learning step in data pre-processing to reduce data dimensionality. The bi-stage FS approach is used in this work to elect the features. At the initial stage, we employed a term weighting scheme (TWS) that assigned a weighted score to each feature by measuring the significance of the features obtained from the pre-processing model using a new weight calculation method called root term frequency-core-inverse exponential frequency (RTF-C-IEF). Next, finding the most relevant and almost optimal feature subset for COVID-19 illness diagnosis is done in the second stage using a freshly developed methodology that was inspired by the way sparrow's behavior. The suggested modification method for the sparrow's algorithm is composed of several stages of advancement. The main objectives are to promote the exploration of the search space and increase the algorithm's variability. In order to evaluate the proposed model, various classifiers were employed on two datasets, each of which had 1446 and 3053 cases, respectively. The experimental and statistical results demonstrate that the proposed IBSSA is significantly superior compared to other comparative optimization algorithms, and it successfully upgrades the shortcomings of the original SSA. Moreover, the IBSSA has the highest accurate performance when compared to other rivals by the SVM classifier, Where, average removed features are 77.99% and 83.5%, with improvement percentages by F1-scores: 84.95% and 95.94 % for both datasets, respectively.

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Section: Covid-19 Patients Categorization Based Ibssa-fsmentioning

confidence: 99%

An Intelligent Feature Selection Approach Based on a Novel Improve Binary Sparrow Search Algorithm for COVID-19 Classification

2023

IJIES

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“…Multiple supervised machine learning algorithms are used to divide the clinical text relevant to Covid-19 into two groups, the first of which is the clinical text itself. The Random Forest, Logistic Regression, Multinomial Naive Bayes, and Bagging classification algorithms were chosen for this study based on a systematic literature review [52] because they are commonly used in medical mining. Furthermore, in this study, they outperform other applied algorithms.…”

Section: Machine Learning For Classification Of Covid-19 Patientsmentioning

confidence: 99%

Automatic Diagnosis of COVID-19 Patients from Unstructured Data Based on a Novel Weighting Scheme

Mahdi¹,

Yuhaniz²

2023

Computers, Materials &Amp; Continua

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The extraction of features from unstructured clinical data of Covid-19 patients is critical for guiding clinical decision-making and diagnosing this viral disease. Furthermore, an early and accurate diagnosis of COVID-19 can reduce the burden on healthcare systems. In this paper, an improved Term Weighting technique combined with Parts-Of-Speech (POS) Tagging is proposed to reduce dimensions for automatic and effective classification of clinical text related to Covid-19 disease. Term Frequency-Inverse Document Frequency (TF-IDF) is the most often used term weighting scheme (TWS). However, TF-IDF has several developments to improve its drawbacks, in particular, it is not efficient enough to classify text by assigning effective weights to the terms in unstructured data. In this research, we proposed a modification term weighting scheme: RTF-C-IEF and compare the proposed model with four extraction methods: TF, TF-IDF, TF-IHF, and TF-IEF. The experiment was conducted on two new datasets for COVID-19 patients. The first dataset was collected from government hospitals in Iraq with 3053 clinical records, and the second dataset with 1446 clinical reports, was collected from several different websites. Based on the experimental results using several popular classifiers applied to the datasets of Covid-19, we observe that the proposed scheme RTF-C-IEF achieves is a consistent performer with the best scores in most of the experiments. Further, the modified RTF-C-IEF proposed in the study outperformed the original scheme and other employed term weighting methods in most experiments. Thus, the proper selection of term weighting scheme among the different methods improves the performance of the classifier and helps to find the informative term.

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Automatic extraction of knowledge for diagnosing COVID-19 disease based on text mining techniques: A systematic review

Cited by 2 publications

References 0 publications

An Intelligent Feature Selection Approach Based on a Novel Improve Binary Sparrow Search Algorithm for COVID-19 Classification

An Intelligent Feature Selection Approach Based on a Novel Improve Binary Sparrow Search Algorithm for COVID-19 Classification

Automatic Diagnosis of COVID-19 Patients from Unstructured Data Based on a Novel Weighting Scheme

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