Research Article
Drug Information Analysis and Dietary Recommendation Using Machine Learning
@INPROCEEDINGS{10.4108/eai.28-4-2025.2358046, author={Gopichand Reddy G and Aswani K and Rupa Sri M and Aalan Babu A}, title={Drug Information Analysis and Dietary Recommendation Using Machine Learning}, proceedings={Proceedings of the 4th International Conference on Information Technology, Civil Innovation, Science, and Management, ICITSM 2025, 28-29 April 2025, Tiruchengode, Tamil Nadu, India, Part II}, publisher={EAI}, proceedings_a={ICITSM PART II}, year={2025}, month={10}, keywords={diet recommendation drug recommendation machine learning nlp cloud deployment flask react healthcare ai}, doi={10.4108/eai.28-4-2025.2358046} }- Gopichand Reddy G
Aswani K
Rupa Sri M
Aalan Babu A
Year: 2025
Drug Information Analysis and Dietary Recommendation Using Machine Learning
ICITSM PART II
EAI
DOI: 10.4108/eai.28-4-2025.2358046
Abstract
To enhance the results for the patients, the personalization of healthcare services has become more notable. In this work, a personalized diet and drug recommendation system provided by a patient through drug name and age has been described. Using machine learning (ML) and natural language processing (NLP), diets are recommended based on diseases and symptoms. To obtain the quality of prediction, Random Forest, Decision Tree, and deep learning models are being compared in the system. In addition, it augments traditional methods by incorporating external drug databases (OpenFDB and DrugBank APIs) coupled with OCR based prescription scanning and cloud computing for real time access. With the framework and system described, users are able to access detailed medication information, along with personalized diet recommendations enabling higher efficiency in healthcare outcomes while minimizing medication adverse effects. According to our results, accuracy in disease prediction and dietary recommendations is high, setting the stage for artificial intelligence (AI) in personalized medicine.
