Personalized Medicine Recommendation System

Abstract: The Personalized Medical Recommendation System represents an innovative approach in the healthcare sector, aiming to provide individualized medical guidance by leveraging patient data, such as medical history, symptoms, lifestyle, and preferences. Traditional healthcare models often rely on standardized treatments, which may overlook the unique health needs of each patient. This system fills that gap by utilizing machine learning algorithms to analyze input data and generate accurate, tailored recommendations, such as disease diagnosis, medication suggestions, personalized workouts, and dietary plans. Designed to optimize patient outcomes and reduce dependency on healthcare providers for routine advice, this system also enhances accessibility, particularly for remote or underserved populations, promoting proactive and preventive healthcare. Through continuous adaptation and data-driven learning, the Personalized Medical Recommendation System aligns with the future of patient-centric care, improving healthcare delivery by making it more effective, accessible, and tailored to individual needs.

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Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2025-05-31
DOI
https://doi.org/10.22214/ijraset.2025.71928
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
0.00
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article

Personalized Medicine Recommendation System

Shreyas Rane
International Journal for Research in Applied Science and Engineering Technology
Artificial Intelligence in Healthcare
article

Personalized Medicine Recommendation System

Shreyas Rane
article en

Abstract

Abstract: The Personalized Medical Recommendation System represents an innovative approach in the healthcare sector, aiming to provide individualized medical guidance by leveraging patient data, such as medical history, symptoms, lifestyle, and preferences. Traditional healthcare models often rely on standardized treatments, which may overlook the unique health needs of each patient. This system fills that gap by utilizing machine learning algorithms to analyze input data and generate accurate, tailored recommendations, such as disease diagnosis, medication suggestions, personalized workouts, and dietary plans. Designed to optimize patient outcomes and reduce dependency on healthcare providers for routine advice, this system also enhances accessibility, particularly for remote or underserved populations, promoting proactive and preventive healthcare. Through continuous adaptation and data-driven learning, the Personalized Medical Recommendation System aligns with the future of patient-centric care, improving healthcare delivery by making it more effective, accessible, and tailored to individual needs.

International Journal for Research in Applied Science and Engineering TechnologyVol. 13(5)
Ruby Hall Clinic (IN)
Openalex Percentile: Top 85%
Artificial Intelligence in Healthcare
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