Disease Prediction based on Symptoms.

**A end to end project - Powered by Django and Machine Learning** - This project aims to provide a web platform to predict the occurrences of disease on the basis of various symptoms. The user can select various symptoms and can find the diseases and consult to the doctor online.

Code for "Graph Neural Network on Electronic Health Records for Predicting Alzheimer’s Disease"

Automatic detection of plant diseases

Heart Disease prediction using 5 algorithms

Multi-modal Graph learning for Disease Prediction (IEEE Trans. on Medical imaging, TMI2022)

AI-based voice-assisted Contact Center for assisting Farmers for their problems.

Final Year Project Diseases Prediction System through Machine Learning. Disease Prediction system with code and documents

The Labinator Medical Symptom Checker helps you understand the possible causes of symptoms in children and adults. It uses the USWDS UI components and programmed in JavaScript.

A menu based multiple chronic disease detection system which will detect if a person is suffering from a severe disease by taking an essential input image.

Healthcare system to predict Diseases based on patient symptoms

ASD diagnosis on ABIDE I

This Project(Not Responsive) is predict the disease based on your Symptoms .Here ,the I use Html, CSS, JavaScript ,PHP ,MySql ,JQuery. .Before Enter to the project at first insert the database into the sql database .Database file is given inside the project . For admin pannel : Username: admin Password: Ab123456 For User pannel : Username: user …

This repository houses machine learning models and pipelines for predicting various diseases, coupled with an integration with a Large Language Model for Diet and Food Recommendation. Each disease prediction task has its dedicated directory structure to maintain organization and modularity.

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Sparrow AI - API for disease diagnostics

CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks

NeuroGNN is a state-of-the-art framework for precise seizure detection and classification from EEG data. It employs dynamic Graph Neural Networks (GNNs) to capture intricate spatial, temporal, semantic, and taxonomic correlations between EEG electrode locations and brain regions, resulting in improved accuracy. Presented at PAKDD '24.

Flask based web app with five machine learning models on the 10 most common disease prediction, covid19 prediction, breast cancer, chronic kidney disease and heart disease predictions with their symptoms as inputs or medical report (pdf format) as input.

Undergrad final year project to predict diseases given any text symptoms.