Health Status Tracker Dashboard: An AI-Driven Approach to Personalized Health Monitoring and Recommendations

In this research, a Health Status Tracker Dashboard that takes advantage of artificial intelligence for personalized health recommendations is developed and implemented. Basic health parameters which include height, weight, age, blood group diet and water intake are collected from users who are then processed through the system to come generate tailored health insights. The dashboard uses build using Node.js for backend processing and allows a user to interact with it visually, showing the representation of health metrics and trends. The study compares the performance of AI driven health recommendations with traditional health monitoring methods and presents a considerable improvement in engagement of users and engagement in health. The results show that recommended personalized AI advice is 37% higher in user compliance to health goals than generic health advice. This research contributes to a new field in intelligent health monitoring systems, and furnishes means for future evolution of personalized digital health platforms.

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Publication Details

Journal
International Journal of Advanced Research in Science Communication and Technology
Published
2025-04-12
DOI
https://doi.org/10.48175/ijarsct-25160
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
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article

Health Status Tracker Dashboard: An AI-Driven Approach to Personalized Health Monitoring and Recommendations

Prashant Patil, Sandesh Karad, Vivaan Kukreja, Prathamesh Pardhi
International Journal of Advanced Research in Science Communication and Technology
Machine Learning in Healthcare
article

Health Status Tracker Dashboard: An AI-Driven Approach to Personalized Health Monitoring and Recommendations

Prashant Patil, Sandesh Karad, Vivaan Kukreja, Prathamesh Pardhi
article en

Abstract

In this research, a Health Status Tracker Dashboard that takes advantage of artificial intelligence for personalized health recommendations is developed and implemented. Basic health parameters which include height, weight, age, blood group diet and water intake are collected from users who are then processed through the system to come generate tailored health insights. The dashboard uses build using Node.js for backend processing and allows a user to interact with it visually, showing the representation of health metrics and trends. The study compares the performance of AI driven health recommendations with traditional health monitoring methods and presents a considerable improvement in engagement of users and engagement in health. The results show that recommended personalized AI advice is 37% higher in user compliance to health goals than generic health advice. This research contributes to a new field in intelligent health monitoring systems, and furnishes means for future evolution of personalized digital health platforms.

International Journal of Advanced Research in Science Communication and Technology
AISSMS Institute of Information Technology
Openalex Percentile: Top 97%
Machine Learning in Healthcare
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Health Status Tracker Dashboard: An AI-Driven Approach to Personalized Health Monitoring and Recommendations — Prashant Patil, Sandesh Karad, et al. · International Journal of Advanced Research in Science Communication and Technology (2025) | TGRS Research Map | TGRS