Medicine & Treatment Recommendation System using Deep Learning
Chetana Mahajan
, Prashant Shimpi
Deep Learning ,Medicine Recommendation Treatment, Recommendation, Healthcare, AI Neural ,Networks
The growing demand for personalized healthcare solutions has led to the development of intelligent systems that assist in medical decision-making. This project focuses on creating a Medicine Recommendation System that utilizes machine learning techniques to recommend suitable medicines based on user inputs such as symptoms or medical conditions. The system leverages a well-structured medical dataset to train a machine learning The integration of deep learning techniques into the healthcare domain has shown significant potential in improving decision-making processes for medical diagnoses and treatment planning. This paper presents a Medicine & Treatment Recommendation System leveraging deep learning algorithms to provide personalized treatment recommendations based on patient data, medical history, symptoms, and diagnosis. By utilizing advanced neural network architectures, such as convolutional neural networks (CNNs) for image-based diagnosis and recurrent neural networks (RNNs) for sequential data analysis, the system efficiently learns complex patterns from diverse healthcare datasets. These include patient demographics, lab test results, medical records, and clinical notes. The system aims to predict the most effective medicines or treatments tailored to individual patients, reducing human error, enhancing clinical decision support, and improving patient outcomes. By continuously updating and training on new datasets, the system ensures scalability and adaptability in evolving medical scenarios. A case study using a publicly available dataset demonstrates the efficacy of the proposed system, showing its capability to recommend accurate treatment plans for various medical conditions.
"Medicine & Treatment Recommendation System using Deep Learning", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.10, Issue 3, page no.b82-b86, March-2025, Available :https://ijsdr.org/papers/IJSDR2503111.pdf
Volume 10
Issue 3,
March-2025
Pages : b82-b86
Paper Reg. ID: IJSDR_300586
Published Paper Id: IJSDR2503111
Downloads: 000238
Research Area: Science and Technology
Country: Jalgaon, Maharatra, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
