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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | Disease Prediction and Drug Recommendation using Machine Learning |
| Authors Name: | Rupali Bhagat , Taniksha Patil , Gourav Barua , S.D. Kamble |
| Unique Id: | IJSDR2404160 |
| Published In: | Volume 9 Issue 4, April-2024 |
| Abstract: | Disease predictions and drug recommendation using machine learning represent a trans-formative frontier in healthcare. Leveraging advanced algorithms, vast datasets, and interdisciplinary collaboration, these systems are poised to revolutionize patient care. This abstract outlines the core principles and promising outcomes of applying machine learning to disease predictions and drug recommendations. Disease pr––edictions and drug recommendation systems are at the forefront of personalized healthcare. With the potential to improve patient outcomes, reduce healthcare costs, and advance medical knowledge, these systems hold great promise for the future of healthcare. While addressing challenges related to data quality, privacy, and fairness is essential, ongoing advancements in technology and healthcare practices continue to propel these systems toward achieving their full potential Large blocks of data must be analyzed and explored by utilizing the data mining procedures in order to uncover significant patterns and trends. Medical databases are one area where the data mining procedures can be utilized. Many people all over the world are struggling with their health and medical diagnoses. Massive amounts of data are produced by hospital information systems (HIS), yet it might be difficult to extract knowledge from diagnosis case data. By just giving the symptoms they are experiencing, patients can quickly learn about the sickness they are experiencing and the medication that can assist, treat it using the approaches utilized in this paper. In this paper, we give drug recommendations relied on ratings and conditions to customers. Four distinct prototypes are utilized to predict the diseases. The Vader tool and sentiment analysis relied on NLP are utilized to analyze the reviews. And finally, probabilistic and weighted average methodologies are utilized to recommend the medications. Each model and strategy utilized in this paper is described in detail. The experimental findings presented in this work can be utilized in future studies and for a variety of different medicinal applications. |
| Keywords: | Disease Prediction, Drug Recommendation, Machine Learning, Healthcare, Personalized Medicine |
| Cite Article: | "Disease Prediction and Drug Recommendation using Machine Learning", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.9, Issue 4, page no.1109 - 1115, April-2024, Available :http://www.ijsdr.org/papers/IJSDR2404160.pdf |
| Downloads: | 000338176 |
| Publication Details: | Published Paper ID: IJSDR2404160 Registration ID:210953 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: 1109 - 1115 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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