[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119386-en":3,"doc-seo-119386-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},119386,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",7,"Healthcare","Drug Recommendation System and Disease Predication using Machine Learning","Machine learning in healthcare enables more efficient disease diagnosis and personalized treatment support. This project proposes a dual-purpose system that predicts probable diseases from patient symptoms, demographic data, and medical history using classification models such as Decision Trees, Random Forest, and Support Vector Machines. After prediction, it recommends suitable drugs by analyzing historical treatment patterns and drug effectiveness. A hybrid recommendation engine applies collaborative filtering and content-based filtering, trained and validated with publicly available datasets to maintain accuracy and relevance.","International Journal of Innovative Research in Science  \nEngineering and Technology (IJIRSET)  \n(A Monthly, Peer Reviewed, Refereed, Scholarly Indexed, Open Access Journal)  \nImpact Factor: 8.699 Volume 14, Issue 4 , April 2025  \n| \u003Cbr>|[www.ijirset.com](www.ijirset.com |A Monthly)[ |A Monthly](www.ijirset.com |A Monthly), Peer Reviewed & Refereed Journal| e-ISSN: 2319-8753| p-ISSN: 2347-6710|\u003Cbr>Volume 14, Issue 4, April 2025\u003Cbr>|DOI: 10.15680/IJIRSET.2025.1404505| |\n| --- |\n| Drug Recommendation System and Disease Predication using Machine Learning\u003Cbr>R.Azhagusundaram, Shaik Mohammad Arif, Shaik Sadhak Basha\u003Cbr>Associate Professor, Department of CSE, Bharath Institute of Higher Education and Research, Selaiyur, Chennai,\u003Cbr>Tamil Nadu, India\u003Cbr>B. Tech Students, Department of CSE, Bharath Institute of Higher Education and Research, Selaiyur, Chennai,\u003Cbr>Tamil Nadu, India\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr> |\n| \u003Cbr>ABSTRACT: In recent years, the integration of machine learning (ML) into healthcare has opened new frontiers in disease diagnosis and personalized medicine. This project presents a dual-purpose system that leverages machine learning algorithms for disease prediction and drug recommendation based on patient symptoms and medical history. The system is designed to analyze user inputs—such as symptoms, demographic information, and prior medical conditions—and predict the most probable disease using classification algorithms like Decision Trees, Random Forest, and Support Vector Machines.Following the disease prediction, the system intelligently recommends appropriate drugs by analyzing historical treatment data and drug effectiveness. Collaborative filtering and contentbased filtering techniques are employed to build a robust drug recommendation engine. The model is trained and validated on publicly available datasets (e.g., Disease-Symptom and Drug-Disease datasets) to ensure accuracy and relevance. |\n| \u003Cbr>KEYWORDS: Disease prediction, Flask web application, Random Forest classifier, fuzzy symptom matching,\u003Cbr>\u003Cbr>\u003Cbr>I. INTRODUCTION\u003Cbr>The advancement of machine learning (ML) and artificial intelligence (AI) technologies has significantly impacted various sectors, with healthcare being one of the most transformative fields. Early diagnosis and proper treatment are crucial for improving patient outcomes, yet traditional diagnostic methods often require extensive time and expertise. In this context, machine learning provides an opportunity to streamline and enhance healthcare delivery through automation, prediction, and intelligent recommendations.This project focuses on the development of a Drug\u003Cbr>IJIRSET©2025 | An ISO 9001:2008 Certified Journal | 9516 |\n\n\n| \u003Cbr>|[www.ijirset.com](www.ijirset.com |A Monthly)[ |A Monthly](www.ijirset.com |A Monthly), Peer Reviewed & Refereed Journal| e-ISSN: 2319-8753| p-ISSN: 2347-6710|\u003Cbr>Volume 14, Issue 4, April 2025\u003Cbr>|DOI: 10.15680/IJIRSET.2025.1404505|\u003Cbr>Recommendation System coupled with Disease Prediction capabilities, using machine learning algorithms. The system takes user inputs such as symptoms, demographic data, and medical history to predict potential diseases. Once the disease is identified, the system recommends appropriate drugs based on historical treatment data and known efficacy. By leveraging classification techniques and recommendation models, the system can support medical professionals and patients in making data-driven decisions.The main objective is to create a smart, efficient, and reliable healthcare assistant that not only predicts diseases with high accuracy but also provides safe and relevant drug suggestions. This innovation has the potential to reduce diagnostic errors, assist in remote healthcare scenarios, and promote personalized medicine.\u003Cbr>\u003Cbr>II. LITERATURE REVIEW |\n| --- |\n| \u003Cbr>Recent studies have demonstrated the growing role of machine learning in healthcare, particularly in disease prediction and drug recommendation. Algorithms like Decision T","cbCair8gM92PnChC","https://ap.wps.com/l/cbCair8gM92PnChC","pdf",1955143,1,"English","en",105,"# Abstract\n# Introduction\n# Literature Review\n# Methodology","[{\"question\":\"What does the system predict and how is prediction performed?\",\"answer\":\"It predicts the most probable disease from user symptoms, demographic information, and medical history. Classification algorithms such as Decision Trees, Random Forest, and Support Vector Machines are used for disease prediction.\"},{\"question\":\"How does the system recommend drugs after disease prediction?\",\"answer\":\"It recommends drugs by analyzing historical treatment data and known drug effectiveness. The approach uses a hybrid drug recommendation engine combining content-based filtering and collaborative filtering.\"},{\"question\":\"What datasets and steps are used to build and validate the model?\",\"answer\":\"The system trains on publicly available datasets such as Disease-Symptom and Drug-Disease datasets. It performs preprocessing including cleaning, normalization, and encoding before training and validation.\"}]","Drug Recommendation System and Disease Predication using Machine Learning | PDF",1785724046,18,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"drug-recommendation-system-and-disease-predication-using-machine-learning","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/healthcare/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/drug-recommendation-system-and-disease-predication-using-machine-learning/119386/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What does the system predict and how is prediction performed?","Question",{"text":74,"@type":75},"It predicts the most probable disease from user symptoms, demographic information, and medical history. Classification algorithms such as Decision Trees, Random Forest, and Support Vector Machines are used for disease prediction.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the system recommend drugs after disease prediction?",{"text":79,"@type":75},"It recommends drugs by analyzing historical treatment data and known drug effectiveness. The approach uses a hybrid drug recommendation engine combining content-based filtering and collaborative filtering.",{"name":81,"@type":72,"acceptedAnswer":82},"What datasets and steps are used to build and validate the model?",{"text":83,"@type":75},"The system trains on publicly available datasets such as Disease-Symptom and Drug-Disease datasets. 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