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ISSN Approved Journal No: 2320-2882 | Impact factor: 7.97 | ESTD Year: 2013
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Published Paper Details:
Paper Title
Disease Prediction from Symptoms Using Machine Learning
Authors
PRATHAMESH J. LONARE, SWAPNIL S. KUMBHARE, MOHAK S. TALODHIKAR, SUYOG S. MADAVI, DR. VANITA BURADKAR
Keywords
Machine Learning, Disease Prediction, SVM, Random Forest, Decision Tree, Classification
Abstract
This Project is all about creating a smart system that can predict diseases like dengue, malaria, and typhoid by analysing symptoms reported by patients. From a predetermined list of symptoms and the associated diagnoses, it employs three distinct machine learning algorithms--Random Forest, Decision Tree, and Support Vector Machine to identify diseases. Each algorithm has gone through extensive training and testing to ensure its accurate and reliable in making predictions. The Random Forest algorithm exhibits superior accuracy in disease prediction when compared to both the Decision Tree and Support Vector Machine models. It reliably produces consistent results by effectively recognizing the appropriate disease based on the symptoms provided by the user, thereby establishing itself as the most efficient model within our framework.
IJCRT's Publication Details
Unique Identification Number - IJCRT2411775
Paper ID - 273136
Page Number(s) - g958-g966
Pubished in - Volume 12 | Issue 11 | November 2024
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Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
PRATHAMESH J. LONARE, SWAPNIL S. KUMBHARE, MOHAK S. TALODHIKAR, SUYOG S. MADAVI, DR. VANITA BURADKAR, "Disease Prediction from Symptoms Using Machine Learning", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 11, pp.g958-g966, November 2024, Available at :http://www.ijcrt.org/papers/IJCRT2411775.pdf
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Submit Manuscript Online Impact Factor: 7.97 Review Results : Within 02-03 Days Paper Publication : Within 02-03 Days
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
ISSN and 7.97 Impact Factor Details
ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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