Smart Healthcare Assistant App Using AI & Data Analysis

This project presents an AI-Based Smart Healthcare Assistant Application developed using Machine Learning and Data Analysis techniques. The system is designed to provide intelligent healthcare support by predicting diseases based on user symptoms and generating personalized healthcare recommendations. The application utilizes machine learning algorithms such as Decision Tree, Random Forest, Support Vector Machine (SVM), Logistic Regression, and Hidden Markov Model (HMM) for disease prediction and analysis. The system integrates frontend and backend technologies along with database management for efficient healthcare data processing. The proposed healthcare assistant improves early disease identification, real-time monitoring, healthcare accessibility, and user awareness. It also supports future enhancements such as wearable device integration, IoT-based monitoring, and advanced AI-driven healthcare analytics. This project was developed as a final year B.Tech Artificial Intelligence and Data Science project under Anna University.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-09
DOI
https://doi.org/10.5281/zenodo.20096395
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
0.00
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article

Smart Healthcare Assistant App Using AI & Data Analysis

Mrs. Mohanapriya T, Ruffin Eunogia D, Kaviya Mary L, Pavithra J et al.
Zenodo (CERN European Organization for Nuclear Research)
Artificial Intelligence in Healthcare
article

Smart Healthcare Assistant App Using AI & Data Analysis

Mrs. Mohanapriya T, Ruffin Eunogia D, Kaviya Mary L, Pavithra J, Mercy P
article en

Abstract

This project presents an AI-Based Smart Healthcare Assistant Application developed using Machine Learning and Data Analysis techniques. The system is designed to provide intelligent healthcare support by predicting diseases based on user symptoms and generating personalized healthcare recommendations. The application utilizes machine learning algorithms such as Decision Tree, Random Forest, Support Vector Machine (SVM), Logistic Regression, and Hidden Markov Model (HMM) for disease prediction and analysis. The system integrates frontend and backend technologies along with database management for efficient healthcare data processing. The proposed healthcare assistant improves early disease identification, real-time monitoring, healthcare accessibility, and user awareness. It also supports future enhancements such as wearable device integration, IoT-based monitoring, and advanced AI-driven healthcare analytics. This project was developed as a final year B.Tech Artificial Intelligence and Data Science project under Anna University.

Zenodo (CERN European Organization for Nuclear Research)
Orthopaedic Research Group (IN)
Openalex Percentile: Top 4%
Artificial Intelligence in Healthcare
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