[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127748-en":3,"doc-seo-127748-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127748,962084926284,"Aurora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Determine the Classification of COVID-19 by Combining the Encoding of Amino Acids with Machine-Learning Models","A novel computational framework enhances COVID-19 strain classification accuracy by integrating amino-acid sequence encoding with machine-learning models. The work targets limitations in existing methods that miss amino-acid sequences as predictive biomarkers. Feature selection using Information Gain (IG) and Analysis of Variance (ANOVA) extracts informative sequence attributes while reducing computational complexity. Using an NGDC-derived dataset of amino-acid sequences across strains and 10-fold cross-validation, the study compares Decision Trees (DT) and Random Forest (RF). RF achieves 98.69% accuracy, outperforming DT’s 89.23%, with robustness linked to its ensemble design.","Determine the Classification ofCOVID-19 by Combining the Encoding of Amino Acids with Machine-Learning Models  \nMr. Anurag Golwalkar1  \nResearch Scholar  \nDepartment of Computer Science and Engineering  \nSAGE University, Indore,(M.P.), India  \nEmail: [agolwelkar@gmail.com](agolwelkar@gmail.com)  \nDr. Abhay Kothari2  \nProfessor  \nDepartment of Computer Science and Engineering  \nSAGE University, Indore,(M.P.), India  \n[Email: abhaykothari333@gmail.com](Email: abhaykothari333@gmail.com)  \nAbstract—In the ongoing battle against COVID-19, a novel approach integrating the encoding of amino acids with advanced machine-learning models offers a promising avenue for enhancing the classification accuracy of the virus strains. The relentless evolution of the virus necessitates robust and adaptable diagnostic tools capable of capturing the genetic intricacies that underpin the disease's transmission and virulence. This study addresses the critical need for refined classification techniques, pinpointing a significant gap in existing methodologies that often overlook the potential of amino acid sequences as predictive biomarkers. Employing a sophisticated feature selection mechanism, this research harnesses the power of Information Gain (IG) and Analysis of Variance (ANOVA) to distill essential features from the amino acid sequences. This process not only illuminates the sequences' predictive capacity but also reduces computational complexity, paving the way for more efficient model training and validation. The dataset, derived from the National Genomics Data Center (NGDC), encompasses a comprehensive array of amino acid sequences associated with various COVID-19 strains, providing a fertile ground for model evaluation through 10-fold cross-validation. The study meticulously evaluates the performance of two machine-learning classifiers: Decision Trees (DT) and Random Forest (RF) . Utilizing IG, the RF classifier demonstrated exceptional proficiency, achieving an accuracy of 98.69%, with similarly high metrics across sensitivity, specificity, and precision. This starkly contrasts with the DT classifier, which, while respectable, lagged behind with an overall accuracy of 89.23%. A parallel examination using ANOVA echoed these findings, with RF maintaining superior performance, albeit with a narrower margin of distinction between the two classifiers. This comparative analysis underscores the RF classifier's robustness, attributable to its ensemble nature, which aggregates insights from multiple decision trees to mitigate overfitting and enhance predictive accuracy. The integration of amino acid encoding with RF, informed by targeted feature selection through IG and ANOVA, presents a potent methodology for COVID-19 strain classification.  \nKey words: COVID-19 , Amino Acids , Machine-Learning, Decision Trees, Random Forest.  \nI. INTRODUCTION  \nThe novel coronavirus (COVID-19), which emerged in late 2019, has posed unprecedented challenges to global health, economies, and societies. As scientists and researchers around the world scramble to understand and combat this virus, the role of innovative technologies and methodologies has become increasingly critical. Among these, the application of machine learning (ML) in the classification and prediction of virus strains has shown promising potential. This paper explores a novel approach to classify COVID-19 by combining the encoding of amino acids with decision trees and Random Forest (RF) machine-learning classification models. This method aims to leverage the intrinsic patterns within the virus's genetic makeup to improve the accuracy and efficiency of diagnosing COVID-19 cases.  \nThe significance of accurately classifying COVID-19 cannot be overstated. Early and accurate detection of the virus is crucial for effective patient management, treatment, and containment measures. Traditional methods for virus classification and detection, while effective, often involve time-consuming processes and may no","cbCainWlokr71gbq","https://ap.wps.com/l/cbCainWlokr71gbq","pdf",442370,1,13,"English","en",105,"# Introduction\n## Problem background and motivation\n## Role of machine learning for strain classification\n## Amino-acid encoding approach\n## Decision Tree vs. Random Forest concepts","[{\"question\":\"What is the main idea of the proposed COVID-19 classification method?\",\"answer\":\"The method combines amino-acid sequence encoding with machine-learning classifiers to capture patterns in viral genetic information for strain classification.\"},{\"question\":\"How are features selected from amino-acid sequences?\",\"answer\":\"The study uses Information Gain (IG) and Analysis of Variance (ANOVA) to distill essential sequence features and reduce computational complexity.\"},{\"question\":\"Which classifier performs better, and what accuracy is reported?\",\"answer\":\"Random Forest outperforms Decision Trees, achieving 98.69% accuracy versus 89.23% for Decision Trees.\"}]","Determine the Classification of COVID-19 by Combining the Encoding of Amino Acids with Machine-Learning Models | 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