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Kaur, Balraj Preet
- COVID-19 Severity Analysis Using Improved Machine Learning Algorithm
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Authors
Affiliations
1 Research Scholar, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
2 Assistant Professor, Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology Patiala, IN
3 Associate Professor, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
1 Research Scholar, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
2 Assistant Professor, Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology Patiala, IN
3 Associate Professor, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 35, No SP (2023), Pagination: 11-19Abstract
The new pandemic produced by the COVID-19 virus has resulted in an overflow of medical treatment in clinical centers all over the world. The fast and exponential growth in the number of COVID-19-infected individuals has necessitated an effective and timely prediction of probable infections and their effects in order to reduce health-care quality overload. As a result, intelligent models are being developed and used to assist medical workers in making more accurate diagnoses concerning the health condition of COVID-19-infected individuals. The purpose of this research is to present an alternative algorithmic approach for predicting the health status of COVID-19 patients in Mexico. Different prediction models were assessed and compared, including Adaboost, gradient boosting machine, random forests, and light gradient boosting machine. Additionally, Grid search hyperparameter optimization is used to improve the algorithm's success rate. The optimal model feature analysis procedure is being carried out. The purpose of this study is to analyses features in terms of feature importance as indicated by SHapely adaptive exPlanations (SHAP) values in order to identify relevant predictive factors that can identify patients at high risk of mortality.Keywords
Machine Learning, COVID-19, Hyperparameter Tuning, SHAP Analysis.References
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- https://www.kaggle.com/marianarfranklin/mexico-covid19-clinical-data/
- COVID-19 Diagnosis Using Machine Learning
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Authors
Affiliations
1 Assistant Professor, Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology Patiala, IN
2 Research Scholar, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
3 Associate Professor, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
1 Assistant Professor, Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology Patiala, IN
2 Research Scholar, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
3 Associate Professor, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 35, No SP (2023), Pagination: 107-113Abstract
Over 4 million individuals have already died as a result of the deadly contagious viral COVID-19 worldwide. The infection can seriously harm the lungs, increasing the chance of fatal health effects. The only way to lower the mortality rate due to this deadly illness and to halt its growth is through early detection. Deep learning has recently come to light as one of the most useful methods for computer aided diagnosis for helping clinicians make correct illness diagnoses. However, deep learning models require a lot of processing, so hardware with TPUs and GPUs is required to execute these models. To create machine learning models that can be used on mobile and peripheral devices, experts are currently working. In this context, the goal of this study is to create a concise Convolution Neural Network-based computer-aided diagnostic system that can be used on devices with limited processing capacity, such as mobile phones and iPads, to identify the presence of the Covid-19 virus in x-ray pictures. On the basis of various assessment parameters, the findings plainly show that the suggested model outperforms other transfer learning models such as Resnet50, Inception, and Xception. According to various evaluation parameters, the findings definitely show that the proposed model outperforms other transfer learning models like Resnet50, Inception, and Xception.Keywords
Deep Learning, CNN, COVID-19, Transfer Learning, Image Enhancement.References
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- Machine Learning Based Heart Disease Prediction Model with GUI Interface
Abstract Views :141 |
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Authors
Affiliations
1 Student, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
2 Research Scholar, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
1 Student, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
2 Research Scholar, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 35, No SP (2023), Pagination: 261-276Abstract
One of the top causes of death around the globe is a heart attack. According to current statistics, one person dies from heart disease every minute, making it one of the primary problems in everyday modern life. The ability to predict the onset of illness at an early stage is extremely difficult nowadays. When used in the healthcare industry, machine learning has the potential to accurately and quickly diagnose diseases. The circumstances under which heart disease may arise are estimated in this study. Medical parameters are characteristics of the datasets utilized. The datasets are analyzed using the Random Forest Algorithm, a machine learning algorithm, in Python. This method makes use of historical patient data from the past to forecast future ones at an early stage, saving lives. In this study, a trustworthy system for predicting heart disease is put into place utilizing a powerful machine learning algorithm called the Random Forest method. This reads a CSV file containing patient record data. After gaining access to the dataset, the procedure is carried out, and a useful heart attack level is generated. The suggested system's benefits include High success rates are attained, along with excellent performance and accuracy rates, flexibility, and adaptability.Keywords
Machine Learning, Artificial Intelligence, Heart Disease.References
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