Severity Prediction of COVID-19 Patients Using Machine Learning Classification Algorithms: A Case Study of Small City in Pakistan with Minimal Health Facility

Gull, Hina; Krishnasamy, Gomathi; Aldossary, May Issa; Iqbal, Sardar Zafar

doi:10.1109/iccc51575.2020.9344984

Cited by 11 publications

(9 citation statements)

References 11 publications

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“…[10]. Our ndings differ from those of the previous study [1], [2], [5] in terms of technique. We currently used only one ML algorithm, i.e., the random forest classi er.…”

Section: Discussioncontrasting

confidence: 99%

“…Therefore, the models with a slight impact would need improvement to achieve a better score. Our ML model performed better than the reference model, having an accuracy of 60% [5]. Our model showed an accuracy between 60% and 70%.…”

Section: Discussionmentioning

confidence: 72%

“…I selected SVM to predict patient severity out of the seven algorithms that were considered and assessed. The model has a 60% accuracy rate and divides the severity of the inpatient into moderate and severe levels [5].…”

Section: Introductionmentioning

confidence: 99%

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A New Prediction Data Model of High-Risk COVID-19 Patients with Smart Notification (HRCP-SN) Using a Hybridized Algorithm

Yenurkar

Mal

2023

Preprint

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A web application designed to predict high-risk patients affected by COVID-19 runs a machine learning model at the backend to generate results. The random forest classification technique is used to predict the high-risk status of patients who are COVID-19 positive and are at the initial stage of infection. We used hybridized algorithms to predict high-risk patients, and the model used the patients’ current underlying health conditions, such as age, sex, diabetes, asthma, hypertension, smoking, and other factors. After data preprocessing and training, the model could predict the severity of the patient with an accuracy of 65-70%. According to some studies, random forest ML models outperform other ML models for solving the challenge of predicting unusual events, such as in this case. Pneumonia, hypertension, diabetes, obesity, and chronic renal disease were the most contributory variables for model implementation. This project will help patients and hospital staff make necessary decisions and actions in advance. This will help healthcare workers arrange resources and hospital areas for high-risk COVID-19 patients. Thus, this study provides an effective and optimized treatment. Using this application and suitable patient data, hospitals can predict whether a patient will require urgent care.

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“…[10]. Our ndings differ from those of the previous study [1], [2], [5] in terms of technique. We currently used only one ML algorithm, i.e., the random forest classi er.…”

Section: Discussioncontrasting

confidence: 99%

Section: Discussionmentioning

confidence: 72%

See 1 more Smart Citation

A New Prediction Data Model of High-Risk COVID-19 Patients with Smart Notification (HRCP-SN) Using a Hybridized Algorithm

Yenurkar

Mal

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…[10]. Our ndings differ from those of the previous study [1], [2], [5] in terms of technique. We currently used only one ML algorithm, I.e., Random Forest Classi er.…”

Section: Discussioncontrasting

confidence: 99%

“…I picked SVM to predict patient severity out of the seven algorithms that were considered and assessed. The model has a 60% accuracy rate and divides the severity of the inpatient into moderate and severe levels [5].…”

Section: Related Workmentioning

confidence: 99%

A New Prediction Data Model of High-Risk COVID-19 Patient with Smart Notification (HRCP-SN) Using Machine Learning Algorithm

Yenurkar

Mal

2022

Preprint

View full text Add to dashboard Cite

A web application designed to predict high-risk patients affected by COVID-19 runs a machine learning (ML) model at the backend to generate results. The random forest classification technique (a type of supervised ML approach) is used to predict the high-risk status of patients who are COVID-19 positive and are at the initial stage of infection. To predict high-risk patients, the model uses the patients’ current underlying health conditions, such as age, sex, diabetes, asthma, hypertension, smoking, and other factors. After data preprocessing and training, the model could predict the severity of the patient with an accuracy of approximately in 65–70%. According to some studies, random forest ML models outperform other ML models for solving the challenge of predicting unusual events such as pneumonia, hypertension, diabetes, obesity, and chronic renal disease were the most contributory variables for model implementation. This project will help patients and hospital staff make necessary decisions and actions in advance. This will help healthcare workers arrange resources and hospital areas for high-risk COVID-19 patients. Thus, this study provides an effective and optimized treatment. Using this application and suitable patient data, hospitals can predict whether a patient will require urgent care.

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Multifactor data analysis to forecast an individual's severity over novel COVID‐19 pandemic using extreme gradient boosting and random forest classifier algorithms

Yenurkar,

Mal,

Nyangaresi

et al. 2023

Engineering Reports

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AI and machine learning are increasingly often applied in the medical industry. The COVID‐19 epidemic will start to spread quickly over the planet around the start of 2020. At hospitals, there were more patients than there were beds. It was challenging for medical personnel to identify the patient who needed treatment right away. A machine learning approach is used to predict COVID‐19 pandemic patients at high risk. To provide input data and output results that execute the machine learning model on the backend, a straightforward Python Flask web application is employed. Here, the XGBoost algorithm, a supervised machine learning method, is applied. In order to predict high‐risk patients based on their current underlying health issues, the model uses patient characteristics as well as criteria like age, sex, health issues including diabetes, asthma, hypertension, and smoking, among others. The XGBoost model predicts the patient's severity with an accuracy of about 98% after data pre‐processing and training. The most important factors to the models are chosen to be age, diabetes, sex, and obesity. Patients and hospital personnel will benefit from this project's assistance in making timely choices and taking appropriate action. This will let medical personnel decide how much time and space to devote to the COVID‐19 high‐risk patients. providing a treatment that is both efficient and ideal. With this programme and the necessary patient data, hospitals may decide whether a patient need immediate care or not.

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Severity Prediction of COVID-19 Patients Using Machine Learning Classification Algorithms: A Case Study of Small City in Pakistan with Minimal Health Facility

Cited by 11 publications

References 11 publications

A New Prediction Data Model of High-Risk COVID-19 Patients with Smart Notification (HRCP-SN) Using a Hybridized Algorithm

A New Prediction Data Model of High-Risk COVID-19 Patients with Smart Notification (HRCP-SN) Using a Hybridized Algorithm

A New Prediction Data Model of High-Risk COVID-19 Patient with Smart Notification (HRCP-SN) Using Machine Learning Algorithm

Multifactor data analysis to forecast an individual's severity over novel COVID‐19 pandemic using extreme gradient boosting and random forest classifier algorithms

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