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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
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| Paper Title: | HealthAssist AI: A Machine Learning Based Personal Health Vault With Symptom-Driven Medical Recommendation |
| Authors Name: | Arnav Upadhyay , Prathmesh Dandwate , Ankit Singh Dhakad , A. Shailja , Prabhakar Sharma |
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IJRTI_211158
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| Published Paper Id: | IJRTI2604039 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | |
| Abstract: | The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) technologies has significantly influenced the healthcare sector by enabling intelligent systems capable of assisting in medical decision-making, disease prediction, and healthcare data management. Despite these advancements, many individuals still face challenges in accessing preliminary healthcare guidance and efficiently managing their medical records. Limited healthcare accessibility, delayed consultations, and fragmented medical documentation remain major issues, particularly in regions with limited healthcare infrastructure. To address these challenges, this research proposes HealthAssist AI, an intelligent healthcare assistance platform that integrates machine learning-based symptom analysis with a secure digital Personal Health Vault for medical record management. The proposed system utilizes supervised machine learning algorithms to analyze symptom–disease relationships and generate predictions for common medical conditions. Algorithms such as Decision Tree, Random Forest, Support Vector Machine, and Naïve Bayes are evaluated to identify the most accurate and interpretable model for disease prediction. The selected model is integrated into a web-based application that enables users to input symptoms and receive predicted health conditions along with general medicine recommendations for common illnesses. In addition to predictive capabilities, the system incorporates a secure digital health record storage module that allows users to upload, organize, and manage their medical documents in a structured timeline-based interface. To ensure data privacy and security, the Personal Health Vault implements encryption mechanisms and controlled access features that protect sensitive health information while allowing temporary sharing with healthcare professionals when required. Experimental results indicate that machine learning-based models can effectively predict common diseases using symptom data with high accuracy. The proposed HealthAssist AI system provides a user-friendly, privacy-focused, and intelligent healthcare support platform that enhances healthcare accessibility and encourages efficient digital medical record management. Future improvements may include integration with clinical datasets, deep learning models, and real-time healthcare monitoring systems. The integration of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare has significantly improved medical decision support systems, disease prediction models, and patient data management. Despite these advancements, many individuals still experience difficulties in accessing preliminary healthcare guidance and managing their personal medical records efficiently. This research proposes HealthAssist AI, an intelligent healthcare assistance system that combines machine learning-based symptom analysis with a secure digital Personal Health Vault for medical record management. The system enables users to input symptoms and receive predicted health conditions along with general medicine recommendations for common illnesses. The proposed system employs supervised machine learning algorithms such as Decision Tree, Random Forest, Support Vector Machine, and Naïve Bayes to analyze symptom–disease relationships and generate predictions. The models are trained using a structured medical dataset containing symptoms and associated diseases. After training, the best-performing model is integrated into a web-based platform that allows real-time inference and disease prediction based on user inputs. In addition to the prediction module, the system incorporates a secure health record storage mechanism where users can upload and manage medical reports in a timeline-based interface. To ensure privacy and data protection, the Personal Health Vault uses encryption techniques and role-based access control for secure storage and controlled sharing of medical records. Experimental evaluation demonstrates that machine learning models can effectively predict common diseases based on symptom patterns and provide useful preliminary guidance. The proposed system aims to enhance healthcare accessibility, encourage digital medical record management, and improve patient awareness through AI-driven health assistance tools. Future work will focus on expanding the dataset, integrating deep learning models, and validating predictions using clinical datasets. |
| Keywords: | HealthAssist AI, Machine Learning in Healthcare, Personal Health Vault, Medical Data Security, Symptom-Based Medicine Recommendation, Digital Health Systems |
| Cite Article: | "HealthAssist AI: A Machine Learning Based Personal Health Vault With Symptom-Driven Medical Recommendation", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.a280-a287, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604039.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2604039
Registration ID:211158
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: a280-a287 Country: RAIPUR, Chattisgarh, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604039 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604039 |
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ISSN: 2456-3315
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