Statistical analysis of blood characteristics of COVID-19 patients and their survival or death prediction using machine learning algorithms

Mazloumi, Rahil; Abazari, Seyed Reza; Nafarieh, Farnaz; Aghsami, Amir; Jolai, Fariborz

doi:10.1007/s00521-022-07325-y

Cited by 7 publications

(8 citation statements)

References 47 publications

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“…The RF model classifies data by summing the results of all trees in the form of several decision trees. The Adaboost model combines several weak classifiers to form a stronger classifier and evaluates data multiple times to improve classification performance (Gulati et al, 2013; Hassan et al, 2018; Li et al, 2018; Liu et al, 2018; Mazloumi et al, 2022; Minz & Mahobiya, 2017; Ragab et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

“…To select an ML classifier for categorizing abnormal RBCs, the classification performance of five multiple classifiers (SVM, DT, KNN, RF, and Adaboost models) was evaluated. The selected five classifiers are generally used in classification problems, and they showed excellent performance in the problem of classifying medical data such as breast cancer and COVID-19 using feature information (Hassan et al, 2018;Liu et al, 2018;Mazloumi et al, 2022;Ragab et al, 2019). The SVM improve classification performance (Gulati et al, 2013;Hassan et al, 2018;Liu et al, 2018;Mazloumi et al, 2022;Minz & Mahobiya, 2017;Ragab et al, 2019).…”

Section: Multiple Classifier Selection and Classificationmentioning

confidence: 99%

“…For the classification of abnormal RBCs, seven types of geometric features are extracted. A classifier optimized for categorizing abnormal RBCs is proposed; it is one of the five types of ML classifier models that have shown excellent performance in the classification problem of the medical field (Gulati et al, 2013; Hassan et al, 2018; Li et al, 2018; Liu et al, 2018; Mazloumi et al, 2022; Minz & Mahobiya, 2017; Ragab et al, 2019). In this paper, we propose an automatic classification system that categorizes normal and abnormal RBCs observed in microscope images and provides results to users.…”

Section: Introductionmentioning

confidence: 99%

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Automatic analysis system for abnormal red blood cells in peripheral blood smears

Gil

Moon

Lee

et al. 2022

Microscopy Res & Technique

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The type and ratio of abnormal red blood cells (RBCs) in blood can be identified through peripheral blood smear test. Accurate classification is important because the accompanying diseases indicated by abnormal RBCs vary. In clinical practice, this task is time-consuming because the RBCs are manually classified. In addition, because the classification depends on the subjective criteria of pathologists, objective classification is difficult to achieve. In this paper, an automatic classification method that is solely based on images of RBCs captured under a microscope and processed using machine learning (ML) is proposed. The size and hemoglobin abnormalities of RBCs were classified by optimizing the criteria used in clinical practice. For morphologically abnormal RBCs classification, used seven geometric features information (major axis, minor axis, ratio of major and minor axis, perimeter, circularity, number of convex hulls, difference between area and convex area) and five types of multiple classifiers (Support Vector Machine, Decision Tree, K-Nearest Neighbor, Random Forest, and Adaboost models). Among was categorized using SVM, highly accurate results (99.9%) were obtained. The classification is performed simultaneously, and results are provided to the user through a graphical user interface (GUI).

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Section: Methodsmentioning

confidence: 99%

Section: Multiple Classifier Selection and Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic analysis system for abnormal red blood cells in peripheral blood smears

Gil

Moon

Lee

et al. 2022

Microscopy Res & Technique

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“…When compared to previous studies, the authors reported the VbAFN scheme obtained an accuracy of 99%, with an error rate of 0.02. Mazloumi et al [17] investigated the use of blood samples, age, gender, and ICU admission to predict patient survival or death features in Wuhan, China. The authors examined various ML techniques from 306 infected Tangji Hospital patients.…”

Section: Covid-19 Infection Within a Variety Of Geographical Location...mentioning

confidence: 99%

Predicting ICU Admission for COVID-19 Patients in Saudi Arabia: A Comparative Study of AdaBoost and Bagging Methods

Ghandorh,

Khan,

Khan

et al. 2024

IJACSA

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COVID-19's high fatality rate and accurately determining the mortality rate within a particular geographic region continue to be significant concerns. In this study, the authors investigated and assessed the performance of two advanced machine learning approaches, Adaptive Boosting (AdaBoost) and Bootstrap Aggregation (Bagging), as strong predictors of COVID-19-related intensive care unit (ICU) admissions within Saudi Arabia. These models may help Saudi health-care organizations determine who is at a higher risk of readmission, allowing for more targeted interventions and improved patient outcomes. The authors found AdaBoost-RF and Bagging-RF methods produced the most precise models, with accuracy rates of 97.4% and 97.2%, respectively. This work, like prior studies, illustrates the viability of developing, validating, and using machine learning (ML) prediction models to forecast ICU admission in COVID-19 cases. The ML models that have been developed have tremendous potential in the fight against COVID-19 in the health-care industry.

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“…A modified cuckoo search algorithm has been used for tournament selection in robot path planning [27]. A new hybrid model has been developed using the conditional mutual information maximization algorithm and the cuckoo search algorithm for the prediction of the disease [28]. The particle swarm optimization method tolerates uncertainty and imprecision to a maximum extent [29].…”

Section: Introductionmentioning

confidence: 99%

Prediction of dementia using machine learning model and performance improvement with cuckoo algorithm

Rajayyan

Mustafa

2023

IJECE

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Dementia is a brain disease that stays in the seventh position of death rate as per the report of the World Health Organization (WHO). Among the various types of dementia, Alzheimer’s disease has more than 70% of cases of dementia. The objective is to predict dementia disease from the open access series of imaging studies (OASIS) dataset using machine learning techniques. Also, the performance of the machine learning model is analyzed to improve the performance of the model using the cuckoo algorithm. In this paper, feature engineering has been focused and the prediction of dementia has been done using the OASIS dataset with the help of data mining techniques. Feature engineering is followed by prediction using the machine learning model Gaussian naïve Bayes (NB), support vector machine, and linear regression. Also, the best prediction model has been selected and done the validation. The evaluation metrics considered for validating the models are accuracy, precision, recall, and F1-Score and the highest values are 95%, 97%, 95%, and 95%. The Gaussian NB has been given these best results. The accuracy of the machine learning models has been increased by eliminating the factors which affect the performance of the models using the cuckoo algorithm.

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Statistical analysis of blood characteristics of COVID-19 patients and their survival or death prediction using machine learning algorithms

Cited by 7 publications

References 47 publications

Automatic analysis system for abnormal red blood cells in peripheral blood smears

Automatic analysis system for abnormal red blood cells in peripheral blood smears

Predicting ICU Admission for COVID-19 Patients in Saudi Arabia: A Comparative Study of AdaBoost and Bagging Methods

Prediction of dementia using machine learning model and performance improvement with cuckoo algorithm

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