Personalized Therapeutic Healthcare Assistant
Article Sidebar
Main Article Content
The Therapeutic Optimization for the Analysis of Personalized Health Measurements aims to develop an intelligent system that analyzes individual health parameters to deliver optimized therapeutic inter- ventions tailored to the unique physiological and lifestyle profile of each user. The system harnesses real-time health metrics, including vitals such as heart rate, blood pressure, sleep cycles, glucose levels, physical activity, and dietary habits, collected through wearable IoT devices and mobile health apps. By leveraging advanced data analytics and machine learning models, the project identifies trends, anomalies, and correlations within this multidimensional dataset.
The therapeutic engine evaluates the effectiveness of treat- ments using dynamic health score mod- eling and adjusts regimens using adaptive algorithms. It integrates guidelines from modern medicine, alternative therapies, and patient preferences to ensure personalized, evidence-based outcomes. A fourth model predicts goal achievement timelines by analyzing features such as caloric balance and hydration efficiency, providing users with actionable feedback using rule based algorithm.The integration of these AI-driven components into a scalable digital platform demonstrates the potential of machine learning in transforming health management. Future enhancements include improving model accuracy, enabling real-time feedback, and deploying the system as an accessible mobile application.
Downloads
References
R. Y. Toledo, “A Food Recommender System Considering Nutritional Information and User Preferences,” in IEEE, 2021.
A. Chen, “Simulation of a Machine Learning Enabled Learning Health System for Risk Prediction Using Synthetic Patient Data,” IEEE Trans- actions on Biomedical Engineering, vol. 69, pp. 347–352, 2022.
M. Kivimaki et al., “Body-Mass Index and Risk of Obesity-Related Complex Multimorbidity: An Observational Multicohort Study,” Re- search Gate, vol. 10, pp. 253–265, 2022.
A. B. Taieb, “Understanding the Risk of Developing Weight-Related Complications Associated with Different BMI Categories: A Systematic Review,” Research Explorer, vol. 6, pp. 14–22, 2022.
A. Yadav, “Workout Prediction using ML,” International Journal of Health, vol. 182, pp. 33–37, 2023.
A. Yadav and A. Jadhav, “Workout Whiz – Your Personalized AI PAL,”
International Journal of AI & Wellness, vol. 4, no. 2, pp. 65–70, 2023.
M. Anusari, “A Single Platform for Meal Plans, Workouts, Yoga Schedules Based on Sri Lankan Lifestyle,” in International Conference on Smart Health Technologies, pp. 98–104, 2024.
D. Gondocs, “AI in Medical Diagnosis: AI Prediction & Human Judg- ment,” Journal of Medical AI Research, vol. 8, no. 2, pp. 121–130, 2024.
D. Gondocs, “AI in Medical Diagnosis: AI Prediction & Human Judg- ment,” Journal of Medical AI Research, vol. 8, no. 2, pp. 121–130, 2024.
Hirushit S., Mr. S. Raja, Suwetha S., and Yazhini J., “AI Powered Personalized Healthcare Recommender,” IEEE Xplore, 2024.
Jennifer Jin, Mira Kim, and Soo Dong Kim, “Personalized Health Assistant with Reinforcement Learning,” IEEE Xplore, 2024.
R. Dehbozorgi et al., “The Application of Artificial Intelligence in the Field of Mental Health: A Systematic Review,” Psychiatry, IEEE Xplore, vol. 25, no. 132, 2025.
Pyla Uma, Chilla Rupa Devi, S. V. S. K. Akshay, Boddepalli Sai Akhil, and Dasi ArunTeja, “Diet Planning and Recommendation System using ML and MERN Stack,” IJSREM, 2024.
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.