Research Article
Personalized Disease Prediction and Medical Recommendation System
@INPROCEEDINGS{10.4108/eai.28-4-2025.2358031, author={E. Sujatha and Kolli Amos Daniel and Atluru Sai Vardhan Reddy and Beri Mothish Kumar and Darga Shaik Mohammad Wasim}, title={Personalized Disease Prediction and Medical Recommendation System}, 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={disease prediction medical recommendation system health care}, doi={10.4108/eai.28-4-2025.2358031} }- E. Sujatha
Kolli Amos Daniel
Atluru Sai Vardhan Reddy
Beri Mothish Kumar
Darga Shaik Mohammad Wasim
Year: 2025
Personalized Disease Prediction and Medical Recommendation System
ICITSM PART II
EAI
DOI: 10.4108/eai.28-4-2025.2358031
Abstract
Customized medicine recommendations have become indispensable to the practice of modern healthcare in recent years, supporting individually designed treatment plans for augmented patient care. This paper plan to develop a recommendation of Personalized Medical and Care System with the full stack technologies like React for frontend development, IntelliJ IDEA for back end development. Based on a patient's medical history, current condition and personal preferences, the system reports real-time personalized medical advice. Its structure utilizes various technologies with the focus on a good usage of data exchange and processing. While IntelliJ IDEA supports flexible back end, it does not have dynamic frontend like react. Key features Object collection and corresponding management Machine learning based recommendation algorithms Userfriendly GUI This paper presents design principles as well as implementation issues and solutions, including integrated machine learning models to enhance recommendation quality. Case studies and feedback from users shows the effectiveness of such a system in practice. Results show a significant improvement in patient engagement and satisfaction among those receiving care recommendations tailored to them.
