[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126878-en":3,"doc-seo-126878-105":30,"detail-sidebar-cat-0-en-105":91},{"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},126878,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Analyzing the Impact of Socioeconomic Factors on Cancer Clinical Trials Accessibility in the U.S. - Using Machine Learning","A machine learning approach analyzes how socioeconomic and geographic conditions influence access to cancer clinical trials in the United States. The work reviews trial accessibility and participation patterns, focusing on disparities tied to income and county-level survival outcomes. Using secondary datasets spanning 2010–2015, the study cleans and preprocesses county-based features, builds multiple regression models, tunes hyperparameters, and evaluates performance with RMSE, MSE, MAE, and R². The results identify key drivers and inform actionable policy recommendations for improving healthcare equity.","April  \n5, 2024  \nAnalyzing the Impact of Socioeconomic Factors on Cancer Clinical Trails Accessibility in the U.S. Using Machine Learning  \nPresenters:  \nKrysta Ray  \nHiromi Honda  \nUnderstanding Cancer Clinical Trials  \n• Clinical trials test new ways to find prevent and treat cancer.  \n• Trail placement and selection is meant to be randomized to prevent bias.  \n• Many socioeconomic and geographical factors are believed to be a barrier to trail access  \n• Predictive modeling may reveal solutions by:  \n– Uncovering patterns and insights to trial access  \n– Identifying the factors that lead to disparities in access  \n– Using insights to propose polices to improve healthcare equity  \nImage 1. Map of U.S. showcasing clinical trials offered using Leaflet  \nPrevious Trial Participation and Access Studies  \n• Few machine learning studies but many statistical studies  \n• Patients with lower income (\u003C50k) were 29% less likely to participate  \n• Higher populated areas were found to conduct more trials  \n• 5-year relative survival for all cancers combined is 14% lower among residents of poorer counties  \n\u003C5%  \nenroll in  \n70%  \npatients are willing  \nMethodology  \n1. Data Collection:  \n– Data collected from secondary data set created by Noah Ripper (US Census Bureau, [ClinicalTrials.gov](ClinicalTrials.gov), and other sources for counties in the US from 2010-2015)  \n2. Data Preprocessing:  \n- Clean and prepare data for analysis  \n- Remove duplicates and handle missing values  \n- Convert data types as needed  \n- Group data by county instead of zip code  \n3. Model Development:  \n-Correlation Analysis  \n-Determine target and features  \n-Regression Models  \n-Determine best performing model  \n-Gradient Boosting  \n-Random Forest  \n-Linear Regression  \n-K Neighbors  \n4. Hyperparameter Tuning  \n-Selecting parameters for optimal results from the model  \nMethodology contd.  \n4. Model Evaluation:  \n-Evaluate model performance using metrics: RMSE, MSE, MAE, R2 score  \n-Iterate through modeling process until best accuracy is received  \n5. Results and Insights:  \n-Analyze and interpret model predictions  \n-Identify socioeconomic patterns that contribute to trail access and participation  \n-Provide actionable insights for the healthcare industry and policymakers  \nData Collection and Preprocessing  \n• A secondary data set created by Noah Ripper includes trail data from the US Census Bureau, [ClinicalTrials.gov](ClinicalTrials.gov), and other sources for counties in the US from 2010-2015  \n• Four goals were identified for data preprocessing:  \n• Initial reading  \n• Type conversion  \n• Missing data handling  \n• Erroneous data handling  \n• Rows were listed by zip code and grouped by county. The sum of studies in each zip code was added to each county  \n• Encoding any string values using a label encoder  \nInitial Data Insights  \n• Socioeconomic factors seemed to correlate highly with the number of studies offered.  \n• Using the correlation and feature importance analysis, we selected the features that affected the target (most to least important) .  \n– popEst2015  \n– PovertyEst  \n– incidenceRate  \n– avgDeathsPerYear  \n– medIncome  \nCorrelation Analysis  \nInitial Data Insights  \nLow: ~> $31408.80  \nMid-Low: ~$31408 .80- $62817.50  \nMid-High: ~$62817.50- $94226.30  \nHigh: ~ $94226.30– 125635.00  \nLow: ~>11 .9  \nMid-Low: ~11 .9-23.7  \nMid-High: ~23.7-35.6  \nHigh: ~35.6-47.4  \nModel Comparison  \n• Four different types of regression models were used to predict where trials would be.  \n• Gradient boosting regression had better overall performance compared to the others.  \n• Preliminary model accuracy was 74.47%  \nComparison of Regression Models  \n\n| Metric | Gradient Boosting | Random Forest | Linear Regression | K Neighbors |\n| --- | --- | --- | --- | --- |\n| R2 Score | 0.7447 | 0.7279 | 0.6716 | 0.6225 |\n| Root Mean\u003Cbr>Squared Error | 77.53 | 80.05 | 87.93 | 94.28 |\n| Mean Squared\u003Cbr>Error | 6010.86 | 6407.50 | 7732.33 | 8887.83 |\n| Mean Accuracy\u003Cbr>Error | ","cbCaimOxUAUslell","https://ap.wps.com/l/cbCaimOxUAUslell","pdf",1015602,1,16,"English","en",105,"# Understanding Cancer Clinical Trials\n## Predictive modeling goals\n# Previous Trial Participation and Access Studies\n## Key disparities and survival differences\n# Methodology\n## Data collection and preprocessing\n## Model development and hyperparameter tuning\n## Model evaluation metrics\n# Results and Insights\n## Correlation and feature importance\n## Regression model comparison","[{\"question\":\"What problem does the presentation address about cancer clinical trial access?\",\"answer\":\"It examines how socioeconomic and geographical factors can create barriers to where clinical trials are offered and how patients participate.\"},{\"question\":\"What data and time range are used for the analysis?\",\"answer\":\"The study uses a secondary dataset created from sources including the US Census Bureau and ClinicalTrials.gov, covering U.S. county data from 2010 to 2015.\"},{\"question\":\"Which model performed best in predicting trial locations and why does it matter?\",\"answer\":\"Gradient boosting regression showed the best overall performance, with preliminary accuracy around 74.47%, helping reveal factors that contribute to access disparities and supporting healthcare equity decisions.\"}]","Analyzing the Impact of Socioeconomic Factors on Cancer Clinical Trials Accessibility in the U.S. - Using Machine Learning | PDF",1785935360,40,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"analyzing-the-impact-of-socioeconomic-factors-on-cancer-clinical-trials-accessibility-in-the-us-using-machine-learning","",{"@graph":36,"@context":85},[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/analyzing-the-impact-of-socioeconomic-factors-on-cancer-clinical-trials-accessibility-in-the-us-using-machine-learning/126878/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the presentation address about cancer clinical trial access?","Question",{"text":75,"@type":76},"It examines how socioeconomic and geographical factors can create barriers to where clinical trials are offered and how patients participate.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and time range are used for the analysis?",{"text":80,"@type":76},"The study uses a secondary dataset created from sources including the US Census Bureau and ClinicalTrials.gov, covering U.S. county data from 2010 to 2015.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best in predicting trial locations and why does it matter?",{"text":84,"@type":76},"Gradient boosting regression showed the best overall performance, with preliminary accuracy around 74.47%, helping reveal factors that contribute to access disparities and supporting healthcare equity decisions.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]