Prediction Model for Mortality Analysis of Pregnant Women Affected With COVID-19

Adib, Quazi Adibur Rahman; Bhuiyan, Md. Shahriar Islam; Raihan, Md. Mohsin Sarker; Shams, Abdullah Bin

doi:10.1109/iccit54785.2021.9689824

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…For instance, we can find a contrast for large values of precision, but small values of recall [ 42 ], having a regular F1 score with its balance metric; i.e., the detectability of the appropriate class decreases (recall), while the reliability of the predictions (precision) increases. Nevertheless, we can find a stable model with a better distribution of its metrics [ 55 ], in which large values for precision and recall were obtained, thus having outstanding values for F1 (close to 94.5%). On the other hand, we can find small values for precision with large values for recall [ 53 ]; i.e., the identification capacity of the appropriate class (recall) is increased, while the reliability of the predictions (precision) decreases.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“… Comparison between the-state-of-the-art (RW1 [ 42 ], RW2 [ 44 ], RW3 [ 53 ], RW4 [ 55 ], RW5 [ 45 ]) and this research. …”

Section: Figurementioning

confidence: 99%

“…Likewise, there is a great variety of studies of this type despite the short time that has elapsed since the pandemic began [ 16 , 41 ]. We can find research around the world about this topic employing IA techniques such as random forest models [ 17 , 42 , 43 , 44 , 45 ], deep learning [ 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 ], decision trees [ 43 , 54 ], support vector machine (SVM) [ 49 , 55 ] and logistic regression procedures [ 49 , 56 ]; which are intended to predict the health status (mortality risk or disease severity) of a COVID-19 infected patient employing factors such as the patients age, weight, gender, physiological conditions, demographic data, travel data, computed tomography, vital signs, symptoms, smoking history, radiological features, clinical features, genetic variants, platelets, laboratory test, D-dimer test, chronic comorbidities and general health information. Meantime, other studies [ 57 ] create models using data analysis techniques with the aim of predicting the need of oxygen therapy in a timely manner in COVID-19 patients; which employed variables like shortness of breath, cough, age and fever.…”

Section: Introductionmentioning

confidence: 99%

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Mortality Analysis of Patients with COVID-19 in Mexico Based on Risk Factors Applying Machine Learning Techniques

et al. 2022

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The new pandemic caused by the COVID-19 virus has generated an overload in the quality of medical care in clinical centers around the world. Causes that originate this fact include lack of medical personnel, infrastructure, medicines, among others. The rapid and exponential increase in the number of patients infected by COVID-19 has required an efficient and speedy prediction of possible infections and their consequences with the purpose of reducing the health care quality overload. Therefore, intelligent models are developed and employed to support medical personnel, allowing them to give a more effective diagnosis about the health status of patients infected by COVID-19. This paper aims to propose an alternative algorithmic analysis for predicting the health status of patients infected with COVID-19 in Mexico. Different prediction models such as KNN, logistic regression, random forests, ANN and majority vote were evaluated and compared. The models use risk factors as variables to predict the mortality of patients from COVID-19. The most successful scheme is the proposed ANN-based model, which obtained an accuracy of 90% and an F1 score of 89.64%. Data analysis reveals that pneumonia, advanced age and intubation requirement are the risk factors with the greatest influence on death caused by virus in Mexico.

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Section: Experiments and Resultsmentioning

confidence: 99%

“… Comparison between the-state-of-the-art (RW1 [ 42 ], RW2 [ 44 ], RW3 [ 53 ], RW4 [ 55 ], RW5 [ 45 ]) and this research. …”

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mortality Analysis of Patients with COVID-19 in Mexico Based on Risk Factors Applying Machine Learning Techniques

et al. 2022

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“…[43], [44], [58], [70], [87], [100] Artificial Neural Networks Artificial Neural Networks (ANNs) employed in maternal healthcare leverage inputs such as MRI scans, electrocardiographic images, echocardiogram images, microscopic images, and ultrasound scans to predict various study targets including fetal state monitoring, risk level prediction, mortality prediction, pregnancy risk prediction, infection prediction, birth mode prediction, and childbirth prediction. These ANNs consist of multiple layers of artificial neurons, which effectively learn intricate patterns and relationships within the diverse data inputs.…”

Section: Table IV Black Box / Deep Learning Models In the Maternal Mo...mentioning

confidence: 99%

Statistical Insights Into Machine Learning-Based Box Models for Pregnancy Care and Maternal Mortality Reduction: A Literature Survey

Neha Margret,

Rajakumar,

Arulalan

et al. 2024

IEEE Access

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Maternal mortality is a major public health concern worldwide. It is the number of preventable deaths that occur each year due to pregnancy and childbirth. The research investigates how machine learning may be used to minimize maternal mortality. Historical data on maternal health is used to develop predictive models, early detection systems, and resource allocation techniques. Machine learning helps to identify risk factors, monitor vital signs, and improve access to care. This allows for targeted interventions and better healthcare delivery. The challenges of data accessibility and model interpretation are addressed, highlighting the ethical and equitable applications of machine learning in maternal healthcare. This study emphasizes the potential of machine learning to reduce maternal mortality rates and the pressing need for its incorporation into healthcare systems worldwide.

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PACTDet - An Artificially Intelligent Approach to Detect Pulmonary Illnesses: Pneumonia, Asthma, COVID-19, and Tuberculosis

Raihan

Raihan²,

Nasif

et al. 2023

Communications in Computer and Information Science

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Prediction Model for Mortality Analysis of Pregnant Women Affected With COVID-19

Cited by 10 publications

References 12 publications

Mortality Analysis of Patients with COVID-19 in Mexico Based on Risk Factors Applying Machine Learning Techniques

Mortality Analysis of Patients with COVID-19 in Mexico Based on Risk Factors Applying Machine Learning Techniques

Statistical Insights Into Machine Learning-Based Box Models for Pregnancy Care and Maternal Mortality Reduction: A Literature Survey

PACTDet - An Artificially Intelligent Approach to Detect Pulmonary Illnesses: Pneumonia, Asthma, COVID-19, and Tuberculosis

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