International Journal of Scientific Research and Engineering Development

The Health care sector generates large amounts of unstructured data in the form of reviews , observations and feedback about patient condition and medications . A manual analysis is very difficult and time consuming analysis of data by recommending suitable drug recommendations to the patient . In this project , we are using algorithms like Natural Language Processing (NLP) and Machine Learning algorithms .This process involves Analyzing the patient symptoms , diagnosis and opinions through clinical reviews for providing suitable drug recommendation. The main objective of this project is to design and develop a patient condition aware drug recommendation system integrated with clinical sentiment analysis using Natural Language Processing and Machine learning algorithms . This project focuses on structured data such as symptoms,medical history and diagnosis along with clinical data such as patient feedback , doctor notes , and medical reviews. The proposed solution of this project involves preprocessing and feature extraction from healthcare data using NLP Techniques. The NLP techniques such as Tokenization,lemmatization and vectorization and machine learning models are to classify patient conditions and recommend suitable drugs based on patient condition data . Additionally , sentiment analysis is performed on clinical reviews to assess patient responses and effective treatment , enabling more informed and personalized drug recommendation . The project is implemented by using python based NLP libraries ,scikit-learn for machine learning model development and classification algorithms for predictive analytics. The final outcome of this project is to recommend suitable drugs to the patient.

Dr.T.Syam Sundar Rao, Sk.Kaleshavali, Sk.Mohammad Jani, M.Hemanth Chowdary, P.Hemanth,"Patient Condition-Aware Drug Recommendation and Clinical Review Sentiment Analysis Using NLP & Machine Learning" International Journal of Scientific Research and Engineering Development, V9(2): Page(490-494) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.