Performance evaluation of selected machine learning algorithms for COVID-19 prediction using routine clinical data: With versus Without CT scan features

Shanbehzadeh, Mostafa; Kazemi-Arpanahi, Hadi; Orooji, Azam; Mobarak, Sara; Jelvay, Saeed

doi:10.4103/jehp.jehp_1424_20

Cited by 3 publications

(2 citation statements)

References 62 publications

(65 reference statements)

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“…Metrics such as accuracy, recall, precision, specificity and F1 score can be calculated from the matrix (57). Furthermore, the generalisability of the model can be evaluated by the Mean Squared Error (MSE) which is a reflection of the degree of underfitting or overfitting (58).…”

Section: Evaluation Of Model Performancementioning

confidence: 99%

Machine learning algorithms in spatiotemporal gait analysis can identify patients with Parkinson’s disease

Fernando,

Pannu,

Natarajan

et al. 2023

Preprint

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Changes to spatiotemporal gait metrics in gait-altering conditions are characteristic of the pathology. This data can be interpreted by machine learning (ML) models which have recently emerged as an adjunct to clinical medicine. However, the literature is undecided regarding its utility in diagnosing pathological gait and is heterogeneous in its approach to applying ML techniques. This study aims to address these gaps in knowledge. This was a prospective observational study involving 32 patients with Parkinson’s disease and 88 ‘normative’ subjects. Spatiotemporal gait metrics were gathered from all subjects using the MetaMotionC inertial measurement unit and data obtained were used to train and evaluate the performance of 10 machine learning models. Principal component analysis and Genetic Algorithm were amongst the feature selection techniques used. Classification models included Logistic Regression, Support Vector Machine, Naïve – Bayes, Random Forest, and Artificial Neural Networks. ML algorithms can accurately distinguish pathological gait in Parkinson’s disease from that of normative controls. Two models which used the Random Forest classifier with Principal Component analysis and Genetic Algorithm feature selection techniques separately, were 100% accurate in its predictions and had an [[EQUATION]] score of 1. A third model using principal component analysis and Artificial neural networks was equally as successful (100% accuracy, [[EQUATION]]). We conclude that ML algorithms can accurately distinguish pathological gait from normative controls in Parkinson’s Disease. Random Forest classifiers, with Genetic Algorithm feature selection are the preferred ML techniques for this purpose as they produce the highest performing model.

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Section: Evaluation Of Model Performancementioning

confidence: 99%

Machine learning algorithms in spatiotemporal gait analysis can identify patients with Parkinson’s disease

Fernando,

Pannu,

Natarajan

et al. 2023

Preprint

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show abstract

“…In the article by Shanbehzadeh M. et al, the dataset comprises 501 case records categorized into two classes: COVID-19 and non-COVID-19. It encompasses 32 columns, each representing diagnostic features [20]. Various machine learning algorithms, including naive Bayes, Bayesian network, random forest (RF), multilayer perceptron, K-star, C4.5, and support vector machine, were used for analysis.…”

Section: Introductionmentioning

confidence: 99%

A Novel COVID-19 Diagnosis Approach Utilizing a Comprehensive Set of Diagnostic Information (CSDI)

Zhunissova,

Dzierżak,

Omiotek

et al. 2023

JCM

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The aim of the study was to develop a computerized method for distinguishing COVID-19-affected cases from cases of pneumonia. This task continues to be a real challenge in the practice of diagnosing COVID-19 disease. In the study, a new approach was proposed, using a comprehensive set of diagnostic information (CSDI) including, among other things, medical history, demographic data, signs and symptoms of the disease, and laboratory results. These data have the advantage of being much more reliable compared with data based on a single source of information, such as radiological imaging. On this basis, a comprehensive process of building predictive models was carried out, including such steps as data preprocessing, feature selection, training, and evaluation of classification models. During the study, 9 different methods for feature selection were used, while the grid search method and 12 popular classification algorithms were employed to build classification models. The most effective model achieved a classification accuracy (ACC) of 85%, a sensitivity (TPR) equal to 83%, and a specificity (TNR) of 88%. The model was built using the random forest method with 15 features selected using the recursive feature elimination selection method. The results provide an opportunity to build a computer system to assist the physician in the diagnosis of the COVID-19 disease.

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Sensitivity and Specificity of Patient-Reported Clinical Manifestations to Diagnose COVID-19 in Adults from a National Database in Chile: A Cross-Sectional Study

Martinez

Muñoz

Guerrero-Nancuante

et al. 2022

Biology

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(1) Background: The diagnosis of COVID-19 is frequently made on the basis of a suggestive clinical history and the detection of SARS-CoV-2 RNA in respiratory secretions. However, the diagnostic accuracy of clinical features is unknown. (2) Objective: To assess the diagnostic accuracy of patient-reported clinical manifestations to identify cases of COVID-19. (3) Methodology: Cross-sectional study using data from a national registry in Chile. Infection by SARS-CoV-2 was confirmed using RT-PCR in all cases. Anonymised information regarding demographic characteristics and clinical features were assessed using sensitivity, specificity, and diagnostic odds ratios. A multivariable logistic regression model was constructed to combine epidemiological risk factors and clinical features. (4) Results: A total of 2,187,962 observations were available for analyses. Male participants had a mean age of 43.1 ± 17.5 years. The most common complaints within the study were headache (39%), myalgia (32.7%), cough (31.6%), and sore throat (25.7%). The most sensitive features of disease were headache, myalgia, and cough, and the most specific were anosmia and dysgeusia/ageusia. A multivariable model showed a fair diagnostic accuracy, with a ROC AUC of 0.744 (95% CI 0.743–0.746). (5) Discussion: No single clinical feature was able to fully confirm or exclude an infection by SARS-CoV-2. The combination of several demographic and clinical factors had a fair diagnostic accuracy in identifying patients with the disease. This model can help clinicians tailor the probability of COVID-19 and select diagnostic tests appropriate to their setting.

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Performance evaluation of selected machine learning algorithms for COVID-19 prediction using routine clinical data: With versus Without CT scan features

Cited by 3 publications

References 62 publications

Machine learning algorithms in spatiotemporal gait analysis can identify patients with Parkinson’s disease

Machine learning algorithms in spatiotemporal gait analysis can identify patients with Parkinson’s disease

A Novel COVID-19 Diagnosis Approach Utilizing a Comprehensive Set of Diagnostic Information (CSDI)

Sensitivity and Specificity of Patient-Reported Clinical Manifestations to Diagnose COVID-19 in Adults from a National Database in Chile: A Cross-Sectional Study

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