[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123856-en":3,"doc-seo-123856-105":30,"detail-sidebar-cat-0-en-105":95},{"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":4,"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},123856,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Exploring post-COVID-19 health effects and features with advanced machine learning techniques - research article summary","COVID-19 recovery is often accompanied by persistent adverse health outcomes shaped by demographic factors and interacting physiological and neurological conditions. The study investigates post-COVID-19 health factors across demographic profiles and quantifies significant correlations among sleep patterns, emotional states, anxiety, and memory. Survey data from COVID-recovered patients in Bangladesh were analyzed with multiple machine learning models to select the best prediction approach. Statistical validation included Chi-square and Pearson’s coefficient, while feature importance was assessed using Gini Index, feature coefficients, information gain, and SHAP values.","Edith Cowan University  \nResearch Online  \nResearch outputs 2022 to 2026  \n12-1-2024  \nExploring post-COVID-19 health effects and features with advanced machine learning techniques  \nMuhammad N. Islam Md S. Islam  \nNahid H. Shourav  \nIftiaqur Rahman Faiz A. Faisal  \nSee next page for additional authors  \nFollow this and additional works at: [https://ro.ecu.edu.au/ecuworks2022-2026](https://ro.ecu.edu.au/ecuworks2022-2026)  \n Part of the Artificial Intelligence and Robotics Commons, and the Diseases Commons  \n10.1038/s41598-024-60504-w  \nIslam, M. N., Islam, M. S., Shourav, N. H., Rahman, I., Faisal, F. A., Islam, M. M., & Sarker, I. H. (2024) . Exploring postCOVID-19 health effects and features with advanced machine learning techniques. Scientific Reports, 14, article 9884. [https://doi.org/10.1038/s41598-024-60504-w](https://doi.org/10.1038/s41598-024-60504-w)  \n[This Journal Article is posted at Research Online.](This Journal Article is posted at Research Online.)[ ](This Journal Article is posted at Research Online.)[https://ro.ecu.edu.au/ecuworks2022-2026/3965](https://ro.ecu.edu.au/ecuworks2022-2026/3965)  \nAuthors  \nMuhammad N. Islam, Md S. Islam, Nahid H. Shourav, Iftiaqur Rahman, Faiz A. Faisal, Md M. Islam, and Iqbal H. Sarker  \nThis journal article is available at Research Online: [https://ro.ecu.edu.au/ecuworks2022-2026/3965](https://ro.ecu.edu.au/ecuworks2022-2026/3965)  \n[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nExploring post‑COVID‑19 health effects and features with advanced machine learning techniques  \nMuhammad Nazrul Islam1*, Md Shofiqul Islam 1, Nahid Hasan Shourav1, Iftiaqur Rahman1, Faiz Al Faisal2, Md Motaharul Islam 3 & Iqbal H. Sarker4  \nCOVID‑19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post‑COVID‑19 state. To achieve these objectives, we have identified the post‑COVID‑19 health factors and based on these factors survey data were collected from COVID‑recovered patients in Bangladesh. Employing diverse machine learning algorithms, we utilised the best prediction model for post‑COVID‑19 factors. Initial findings from statistical analysis were further validated using Chi‑square to demonstrate significant relationships among these elements. Additionally, Pearson’s coefficient was utilized to indicate positive or negative associations among various physiological and neurological factors in the post‑COVID‑19 state. Finally, we determined the most effective machine learning model and identified key features using analytical methods such as the Gini Index, Feature Coefficients, Information Gain, and SHAP Value Assessment. And found that the Decision Tree model excelled in identifying crucial features while predicting the extent of post‑COVID‑19 impact.  \nKeywords COVID-19, Pandemic, Machine learning, Statistical analysis, Chi-square, Pearson’s coefficient  \nIt is 2022-2023, and with the blessing of medical science, after the disastrous era of COVID-19, the world is finally seemingly healing from its wounds. But its deep-rooted adversities are still haunting the lives of the affected ones by the post-COVID trauma1–3. After a year of recovery, patients still find it challenging to return to everyday life. Many physical and Neurological factors indicate that vulnerabilities such as depression, anxiety, weakness, sleeplessness, etc., have increased alarmingly. Looking at the same person before and after their fight wit","cbCaism5gosvMXRz","https://ap.wps.com/l/cbCaism5gosvMXRz","pdf",7040457,1,27,"English","en",105,"# Research aims\n## Data collection and participants\n# Methods and validation\n## Machine learning models\n## Statistical analysis and correlation testing\n# Feature importance and results\n## Feature evaluation techniques\n## Best-performing model and key features","[{\"question\":\"What post-COVID health aspects does the research focus on?\",\"answer\":\"It examines physiological and neurological factors such as sleep patterns, emotional states, anxiety, and memory, and how these relate to demographic characteristics like gender and age.\"},{\"question\":\"How was the data used to model post-COVID outcomes?\",\"answer\":\"Survey data were collected from COVID-recovered patients in Bangladesh, and multiple machine learning algorithms were applied to identify the best prediction model for post-COVID factors.\"},{\"question\":\"Which statistical methods were used to confirm relationships among factors?\",\"answer\":\"Chi-square test assessed significant relationships among elements, while Pearson’s coefficient was used to indicate positive or negative associations between physiological and neurological factors.\"},{\"question\":\"How were key features identified and which model performed best?\",\"answer\":\"Feature importance was evaluated using the Gini Index, feature coefficients, information gain, and SHAP value assessment. The Decision Tree model excelled at identifying crucial features while predicting the extent of post-COVID-19 impact.\"}]","Exploring post-COVID-19 health effects and features with advanced machine learning techniques - research article summary | PDF",1785818914,68,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"exploring-post-covid-19-health-effects-and-features-with-advanced-machine-learning-techniques-research-article-summary","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/exploring-post-covid-19-health-effects-and-features-with-advanced-machine-learning-techniques-research-article-summary/123856/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What post-COVID health aspects does the research focus on?","Question",{"text":75,"@type":76},"It examines physiological and neurological factors such as sleep patterns, emotional states, anxiety, and memory, and how these relate to demographic characteristics like gender and age.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the data used to model post-COVID outcomes?",{"text":80,"@type":76},"Survey data were collected from COVID-recovered patients in Bangladesh, and multiple machine learning algorithms were applied to identify the best prediction model for post-COVID factors.",{"name":82,"@type":73,"acceptedAnswer":83},"Which statistical methods were used to confirm relationships among factors?",{"text":84,"@type":76},"Chi-square test assessed significant relationships among elements, while Pearson’s coefficient was used to indicate positive or negative associations between physiological and neurological factors.",{"name":86,"@type":73,"acceptedAnswer":87},"How were key features identified and which model performed best?",{"text":88,"@type":76},"Feature importance was evaluated using the Gini Index, feature coefficients, information gain, and SHAP value assessment. 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