International Scientific Journal of Engineering and Management

An International Scholarly || Multidisciplinary || Open Access || Indexing in all major Database & Metadata
The journal follows the UGC Guidelines and is evaluated for inclusion in the Web of Science
ISSN: 2583-6129

Impact Factor: 7.839

Health Diagnosis Using AI

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  • Create Date 17 December 2025
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Health Diagnosis Using AI

 

 

Manasa P

Sri Shakthi Institute of Engineering and Technology, India

Email: manasap24cs@srishakthi.ac.in

 

Kaviyavikashini J S

Sri Shakthi Institute of Engineering and Technology, India

Email: kaviyavikashinijs24cs@srishakthi.ac.in

 

Jaya Varsha R

Sri Shakthi Institute of Engineering and Technology, India

Email: jayavarshar24cs@srishakthi.ac.in

 

Harinie V K

Sri Shakthi Institute of Engineering and Technology, India

Email: harinievk24cs@srishakthi.ac.in

 

Hemanth M

Sri Shakthi Institute of Engineering and Technology, India

Email: hemanthm24cs@srishakthi.ac.in

 

 

ABSTRACT

The proposed Health Diagnosis Using AI system is an intelligent digital platform designed to assist in the early detection, analysis, and prediction of various health conditions. In today’s rapidly evolving healthcare environment, individuals and medical professionals face challenges in identifying symptoms, interpreting medical data, and making timely decisions. Traditional manual diagnosis often leads to delays, human errors, and inconsistent evaluations. The proposed AI-based system addresses these issues by providing a centralized, data-driven, and user-friendly platform capable of analyzing patient records, symptoms, and medical parameters to deliver accurate diagnostic insights. Using machine learning models, real-time data processing, and secure database connectivity, the system ensures efficient data flow and reliable predictions for users at every level.

Developed with modern technologies, the Health Diagnosis Using AI platform emphasizes precision, secure data handling, responsive design, and efficient backend operations. Its modular architecture and cloud integration make it scalable for future enhancements, such as predictive analytics, AI-based treatment recommendations, and integration with wearable health devices. By combining automation, intelligence, and modern healthcare principles, the system enhances diagnostic accuracy, reduces manual effort, and provides timely support, making it a valuable tool for improving healthcare outcomes across various use cases.


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