[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125384-en":3,"doc-seo-125384-105":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},125384,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Automated Classification of Health Records for Disease Prediction Using NLP and Machine Learning","Healthcare can achieve higher efficiency and accuracy by predicting diseases from electronic health records through automated extraction of actionable knowledge. By combining structured fields with unstructured clinical text, the approach generates stronger health predictions from EHR data. Natural language processing is used for clinical text preparation alongside three principal machine learning models: Support Vector Machines, Random Forest, and Neural Networks, plus additional classifiers. Public health data supports evaluation using precision, recall, and F1-score metrics.","20(2): S2: 177-183, 2025  \n[www.thebioscan.com](www.thebioscan.com)  \nAutomated Classification of Health Records for Disease Prediction Using NLP and Machine Learning  \n1 Dr. S.Uma  \nProfessor, Department of Computer Science, Dr. N.G.P. Arts and Science College, Coimbatore  \n[s.umacbe9@gmail.com](s.umacbe9@gmail.com)  \n2 Ms. R.Gayathri  \nPh.D., Research Scholar, Department of Computer Science, CMS College of Science and Commerce, Coimbatore  \n[gayarajesh25@gmail.com](gayarajesh25@gmail.com)  \n3. Ms. D.Sophia Margaret  \nAssistant Professor, Department of Computer Application, Sambhram Academy of Management Studies Bangalore.  \n[abigailsofi@gmail.com](abigailsofi@gmail.com)  \n4 Mr. Ajay P R  \nFinal UG Computer Science, Department of Computer Science, Dr.N.G.P Arts and Science College  \n[ajaypr413@gmail.com](ajaypr413@gmail.com)  \nDOI: 10.63001/tbs.2025.v20.i02.S2.pp177-183  \nKEYWORDS  \nAutomated Classification, Health Records, Disease Prediction,  \nNatural Language Processing, Machine Learning, Electronic Health Records,  \nPredictive Analytics, NLPbased Classification, Healthcare Analytics, Disease Detection  \nReceived on: 12-02-2025 Accepted on:  \n15-03-2025 Published on:  \n25-04-2025  \nABSTRACT  \nHealthcare industry efficiency and accuracy can be enhanced through disease prediction when using such electronic health records to extract meaningful knowledge. Applying structured and unstructured medical information enables the system to generate improved health predictions from EHR data. The clinical text preparation uses natural language processing alongside three main machine learning models including Support Vector Machines (SVM), Random Forest and Neural Networks along with other algorithms for classification functions. The system utilizes public health data to measure its accuracy performance through precision, recall and F1 scores evaluation.  \nINTRODUCTION  \nThe rise of the use of electronic health records (EHRs) has been massive in the healthcare industry resulting from the shift from paper to computer system usage. Due to the inclusion of demographics, diagnostic results, treatment history, and clinical notes, which are essential to medical decision-making, patient data contained in EHRs is highly valuable. On one hand, it presents  \na challenge for the healthcare sector and on the other hand, an opportunity [1-3] .  \nIn specific, predicting diseases from EHR could completely transform healthcare through early disease prediction, optimization of treatment steps and finally better patient outcomes. Although, structured data like laboratory tests results and patient demographics is easier to deal with, the unstructured clinical text, Dr's notes, discharge summaries and radiology  \nreports contain important information which may be difficult to manually extract. In the past, medical professionals would simply rely on their accumulated knowledge and expertise to interpret the same records with the understanding that it is not scalable and prone to human error.  \nThe unstructured text processing needs Natural Language Processing (NLP) because it functions as a powerful tool to solve these problems. The analysis through NLP enables extraction of meaningful data from clinical documentations which supports identification of essential symptoms alongside diagnostic outputs alongside treatment protocols for disease prediction systems [14] . Common ML algorithms for classification are used for prediction task like Random forest, support vector machine (SVM) and deep neural network (DNN), but there has been an area of research on the question of how to best complement the features of NLP and ML together to enhance disease prediction.  \nThis paper evaluates the benefits along with shortcomings of disease prediction methodologies that use automated classification systems based on EHRs for disease identification through combination of structured and unstructured data sources. The research implements modern NLP preprocessing approaches to","cbCaidhiXaUK2Ov6","https://ap.wps.com/l/cbCaidhiXaUK2Ov6","pdf",512040,1,7,"English","en",105,"# Introduction\n## Problem Background: EHR Adoption and Data Value\n## Challenges: Structured vs. Unstructured Clinical Text\n# Methodology Overview\n## NLP-Based Clinical Text Preparation\n## Classification Models for Prediction\n# Novelty and Contribution","[{\"question\":\"How does the approach use electronic health records for disease prediction?\",\"answer\":\"It extracts knowledge from EHR data by combining structured information (e.g., demographics and diagnostic results) with unstructured clinical text to improve prediction quality.\"},{\"question\":\"Which NLP and machine learning components are used in the system?\",\"answer\":\"NLP is applied to preprocess clinical text, while classification is performed using models such as Support Vector Machines, Random Forest, and Neural Networks, along with other algorithms.\"},{\"question\":\"How is the accuracy of the disease prediction evaluated?\",\"answer\":\"The system measures performance using precision, recall, and F1-score based on public health data.\"}]","Automated Classification of Health Records for Disease Prediction Using NLP and Machine Learning | PDF",1785898592,18,{"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},"automated-classification-of-health-records-for-disease-prediction-using-nlp-and-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/automated-classification-of-health-records-for-disease-prediction-using-nlp-and-machine-learning/125384/",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},"How does the approach use electronic health records for disease prediction?","Question",{"text":75,"@type":76},"It extracts knowledge from EHR data by combining structured information (e.g., demographics and diagnostic results) with unstructured clinical text to improve prediction quality.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which NLP and machine learning components are used in the system?",{"text":80,"@type":76},"NLP is applied to preprocess clinical text, while classification is performed using models such as Support Vector Machines, Random Forest, and Neural Networks, along with other algorithms.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the accuracy of the disease prediction evaluated?",{"text":84,"@type":76},"The system measures performance using precision, recall, and F1-score based on public health data.","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":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"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"]