Development of a Multi-modal Severity Prediction System for Covid-19 using Machine Learning Algorithms
Keywords:Covid-19 diagnosis, Severity prediction, Machine learning, CNN, Deep learning, Multi-modal
Medical systems all over the world have been devastated by the covid19 pandemic. Even abundant and wealthy countries have struggled a lot. As of August, 2022, number of corona virus cases has been reached to almost 588 million worldwide reported to WHO. With automation at the level of covid19 severity prediction can improve healthcare delivery in parts of the world where access to skilled experts is limited. It can also help in resource management and reducing mortality rate.
Method: In this research, the researchers designed and developed a novel multimodal framework for covid19 severity prediction with a high precision capacity including decisions from medical imaging and clinical factors including patient details, co morbidities and blood results. The researchers explored oversampling methods SMOTE and ROC with SVM, Decision Tree, Random Forest and ANN classifiers for predicting severity using clinical factors. Image enhancement methods gamma correction and AHE explored with ChexNet model for severity prediction through X-ray images. Performance of the predictions has been evaluated using accuracy, precision, sensitivity, and F1-score.
Results: The researchers achieved superior prediction using RF classifier with SMOTE method for text dataset with accuracy of 96%. For X-ray image dataset ChexNet with AHE achieved 87% accuracy. Infection severity inversely proportional to clinical factors LYP, LY,MOP,CA, ALB and ALG where as it is directly proportional to AST, ALT,DD,CRP,LDH,BUN,CR,MCH,GLU,TBIL and WBC.
In the future, performance of the image model may be improved by concatenating multi scale features from different layers of CNN to increase representation power of the CNN model. Again channel attention may be beneficial to improve model performance.
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