Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models

Huyut, Mehmet Tahir

doi:10.1016/j.irbm.2022.05.006

Cited by 28 publications

(22 citation statements)

References 50 publications

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“…Running various ML models to diagnose COVID-19 with the RBV parameters, Soltan et al [25] found the XGBoost method to be the most successful model with 85% sensitivity and 90% precision. Huyut [53] used 28 routine blood values with age on a variety of supervised ML models to detect a large population of severely and mildly infected COVID-19 patients. The models with the highest AUC in identifying mildly infected patients were local weighted-learning (0.95%), Kstar (0.91%), Naïve bayes (0.85%), and K nearest neighbor (0.75%).…”

Section: Discussionmentioning

confidence: 99%

Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network

Huyut

Velichko

2022

Sensors

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Since February 2020, the world has been engaged in an intense struggle with the COVID-19 disease, and health systems have come under tragic pressure as the disease turned into a pandemic. The aim of this study is to obtain the most effective routine blood values (RBV) in the diagnosis and prognosis of COVID-19 using a backward feature elimination algorithm for the LogNNet reservoir neural network. The first dataset in the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 tests. The LogNNet-model achieved the accuracy rate of 99.5% in the diagnosis of the disease with 46 features and the accuracy of 99.17% with only mean corpuscular hemoglobin concentration, mean corpuscular hemoglobin, and activated partial prothrombin time. The second dataset consists of a total of 3899 patients with a diagnosis of COVID-19 who were treated in hospital, of which 203 were severe patients and 3696 were mild patients. The model reached the accuracy rate of 94.4% in determining the prognosis of the disease with 48 features and the accuracy of 82.7% with only erythrocyte sedimentation rate, neutrophil count, and C reactive protein features. Our method will reduce the negative pressures on the health sector and help doctors to understand the pathogenesis of COVID-19 using the key features. The method is promising to create mobile health monitoring systems in the Internet of Things.

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Section: Discussionmentioning

confidence: 99%

Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network

Huyut

Velichko

2022

Sensors

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“…ML and AI methods select the relevant biomarkers, revealing their predictive importance and consistently detecting their interactions with each other. Moreover, the diagnostic performance of these methods has the ability to be improved [ 9 , 10 , 11 ]. AI studies for the early detection, diagnosis and prognosis of COVID-19 relied on computed tomography (CT) and RBVs.…”

Section: Introductionmentioning

confidence: 99%

“…However, imaging-based solutions are costly and require specialized equipment. Machine learning (ML) and AI studies based on RBVs features are a more economical and rapid alternative method for the early detection, diagnosis and prognosis of COVID-19 [ 7 , 11 , 12 ]. Previous studies have indicated that this disease can accompany multi-organ dysfunction and cause a variety of symptoms [ 3 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…The previous studies detected the clinical significance of changes in the routine blood values (RBVs) in the diagnosis and prognosis of infectious diseases [ 1 , 2 , 4 , 5 , 30 , 31 ]. However, Jiang et al [ 32 ], Zheng et al [ 33 ] and Huyut [ 11 ] noted that information on early predictive RBVs should be supplemented with large samples, especially for severe and fatal cases of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

“…The early detection of patients in pandemics is an important but clinically difficult process in terms of morbidity and mortality [ 14 , 24 ]. The diagnosis and prognosis of COVID-19 with the use of advanced devices can provide support in improving patient comfort, health system and tackling economic inadequacies [ 6 , 11 , 12 ]. In this context, studies are carried out to diagnose and determine the severity of the disease in the early period by using ML and AI-based methods as well as RBVs data [ 7 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application

Velichko

Huyut

Belyaev

et al. 2022

Sensors

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Healthcare digitalization requires effective applications of human sensors, when various parameters of the human body are instantly monitored in everyday life due to the Internet of Things (IoT). In particular, machine learning (ML) sensors for the prompt diagnosis of COVID-19 are an important option for IoT application in healthcare and ambient assisted living (AAL). Determining a COVID-19 infected status with various diagnostic tests and imaging results is costly and time-consuming. This study provides a fast, reliable and cost-effective alternative tool for the diagnosis of COVID-19 based on the routine blood values (RBVs) measured at admission. The dataset of the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 test results and 51 routine blood values. In this study, 13 popular classifier machine learning models and the LogNNet neural network model were exanimated. The most successful classifier model in terms of time and accuracy in the detection of the disease was the histogram-based gradient boosting (HGB) (accuracy: 100%, time: 6.39 sec). The HGB classifier identified the 11 most important features (LDL, cholesterol, HDL-C, MCHC, triglyceride, amylase, UA, LDH, CK-MB, ALP and MCH) to detect the disease with 100% accuracy. In addition, the importance of single, double and triple combinations of these features in the diagnosis of the disease was discussed. We propose to use these 11 features and their binary combinations as important biomarkers for ML sensors in the diagnosis of the disease, supporting edge computing on Arduino and cloud IoT service.

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Investigating the performance of machine learning algorithms in predicting the survival of COVID‐19 patients: A cross section study of Iran

Yazdani

Bigdeli

Zahmatkeshan

2023

Health Science Reports

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Background and Aims Like early diagnosis, predicting the survival of patients with Coronavirus Disease 2019 (COVID‐19) is of great importance. Survival prediction models help doctors be more cautious to treat the patients who are at high risk of dying because of medical conditions. This study aims to predict the survival of hospitalized patients with COVID‐19 by comparing the accuracy of machine learning (ML) models. Methods It is a cross‐sectional study which was performed in 2022 in Fasa city in Iran country. The research data set was extracted from the period February 18, 2020 to February 10, 2021, and contains 2442 hospitalized patients' records with 84 features. A comparison was made between the efficiency of five ML algorithms to predict survival, includes Naive Bayes (NB), K‐nearest neighbors (KNN), random forest (RF), decision tree (DT), and multilayer perceptron (MLP). Modeling steps were done with Python language in the Anaconda Navigator 3 environment. Results Our findings show that NB algorithm had better performance than others with accuracy, precision, recall, F‐score, and area under receiver operating characteristic curve of 97%, 96%, 96%, 96%, and 97%, respectively. Based on the analysis of factors affecting survival, heart disease, pulmonary diseases and blood related disease were the most important disease related to death. Conclusion The development of software systems based on NB will be effective to predict the survival of COVID‐19 patients

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Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models

Cited by 28 publications

References 50 publications

Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network

Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network

Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application

Investigating the performance of machine learning algorithms in predicting the survival of COVID‐19 patients: A cross section study of Iran

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