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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM50712

Predictive Medicine Recommendation System Powered by Machine Learning

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM50712

Volume:

9 |

6 |

Jun 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/ijsrem50712

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

MANB

Mr. Akshay Nayak B.S

RC

Rindu C

SM

Sharath M

SCA

Samarth C Acharya

SBR

Sharmada B R

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Abstract

– The Predictive Medicine Recommendation System Powered by Machine learning (ML), a system where intelligently suggests suitable medications based on user-provided inputs, using machine learning techniques to enhance accuracy and relevance symptoms or medical conditions. By integrating advanced classification algorithms such as Support Vector Machines (SVM), Random Forest, and Naive Bayes, the system aims to reduce prescription errors, enhance diagnostic efficiency, and support personalized healthcare delivery. The platform supports early disease prediction, medication advice, and risk mitigation by analysing input from electronic health records (EHRs) and user-reported symptoms. Results demonstrate high accuracy in disease classification and medicine recommendation. This system represents a step forward in personalized, technology-enabled healthcare.

KEYWORDS

– Machine Learning Disease Prediction Medicine Recommendation Personalized Healthcare

Cite This Article

Mr. Akshay Nayak B.S, Rindu C, Sharath M, Samarth C Acharya, Sharmada B R, "Predictive Medicine Recommendation System Powered by Machine Learning," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 6, Jun 2025. https://doi.org/10.55041/ijsrem50712

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Publication Date

6/16/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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