Multifactor data analysis to forecast an individual's severity over novel COVID‐19 pandemic using extreme gradient boosting and random forest classifier algorithms
Abstract 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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-12-01
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| Series: | Engineering Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/eng2.12678 |
| _version_ | 1797405570364342272 |
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| author | Ganesh Keshaorao Yenurkar Sandip Mal Vincent O. Nyangaresi Anshul Hedau Prajwal Hatwar Shreyas Rajurkar Juli Khobragade |
| author_facet | Ganesh Keshaorao Yenurkar Sandip Mal Vincent O. Nyangaresi Anshul Hedau Prajwal Hatwar Shreyas Rajurkar Juli Khobragade |
| author_sort | Ganesh Keshaorao Yenurkar |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-09T03:11:55Z |
| format | Article |
| id | doaj.art-6a756d9b637749ef8de679af1e1a8042 |
| institution | Directory Open Access Journal |
| issn | 2577-8196 |
| language | English |
| last_indexed | 2024-03-09T03:11:55Z |
| publishDate | 2023-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj.art-6a756d9b637749ef8de679af1e1a80422023-12-04T01:19:21ZengWileyEngineering Reports2577-81962023-12-01512n/an/a10.1002/eng2.12678Multifactor data analysis to forecast an individual's severity over novel COVID‐19 pandemic using extreme gradient boosting and random forest classifier algorithmsGanesh Keshaorao Yenurkar0Sandip Mal1Vincent O. Nyangaresi2Anshul Hedau3Prajwal Hatwar4Shreyas Rajurkar5Juli Khobragade6School of Computing Science & Engineering VIT Bhopal University Bhopal IndiaSchool of Computing Science & Engineering VIT Bhopal University Bhopal IndiaComputer Science & Engineering Jaramogi Oginga Odinga University of Science & Technology Bondo KenyaComputer Technology Yeshwantrao Chavan College of Engineering, Wanadongri Nagpur IndiaComputer Technology Yeshwantrao Chavan College of Engineering, Wanadongri Nagpur IndiaComputer Technology Yeshwantrao Chavan College of Engineering, Wanadongri Nagpur IndiaComputer Technology Yeshwantrao Chavan College of Engineering, Wanadongri Nagpur IndiaAbstract 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.https://doi.org/10.1002/eng2.12678COVID‐19Herokuhigh‐risk patientsmachine learningPython flaskrandom forest classifier |
| spellingShingle | Ganesh Keshaorao Yenurkar Sandip Mal Vincent O. Nyangaresi Anshul Hedau Prajwal Hatwar Shreyas Rajurkar Juli Khobragade Multifactor data analysis to forecast an individual's severity over novel COVID‐19 pandemic using extreme gradient boosting and random forest classifier algorithms Engineering Reports COVID‐19 Heroku high‐risk patients machine learning Python flask random forest classifier |
| title | Multifactor data analysis to forecast an individual's severity over novel COVID‐19 pandemic using extreme gradient boosting and random forest classifier algorithms |
| title_full | Multifactor data analysis to forecast an individual's severity over novel COVID‐19 pandemic using extreme gradient boosting and random forest classifier algorithms |
| title_fullStr | Multifactor data analysis to forecast an individual's severity over novel COVID‐19 pandemic using extreme gradient boosting and random forest classifier algorithms |
| title_full_unstemmed | Multifactor data analysis to forecast an individual's severity over novel COVID‐19 pandemic using extreme gradient boosting and random forest classifier algorithms |
| title_short | Multifactor data analysis to forecast an individual's severity over novel COVID‐19 pandemic using extreme gradient boosting and random forest classifier algorithms |
| title_sort | multifactor data analysis to forecast an individual s severity over novel covid 19 pandemic using extreme gradient boosting and random forest classifier algorithms |
| topic | COVID‐19 Heroku high‐risk patients machine learning Python flask random forest classifier |
| url | https://doi.org/10.1002/eng2.12678 |
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