Deep Learning Models for the Early Detection of Parkinson’s Disease using the motor-based symptoms.

Detection of Degree of Parkinsonism via the Spiral Test

Parkinson's disease clinical study app

Python library for real-time neural data classification with Deep Learning

Analytics done on clinical healthcare parkinsons analytics

Parkinson's Progression Marker Initiative data science challenge, 2016

The project strives to predict the risk of Parkinson's Disease progression in the patient based on the evaluation of baseline motor and non-motor symptoms of the patients via machine learning approach.

An iOS App that acts as a customizable metronome. Helpful for establishing a walking rhythm for people with Parkinson's Disease.

Built a Parkinson's Disease Detection tool using SVM

Simple self measurement of parkinsons symptoms

MultiAgent-Based Model (MABM) designed to simulate Parkinson's disease using Repast Simphony.

Adaptive deep learning models for Parkinson’s diagnosis using smartwatch sensor data (PADS Dataset)

BPF Parkinson's disease validation — PPMI expression, genotype, and prodromal detection (AUC 0.766)

A simple Flask API for predicting whether a person could be a Parkinson's patient or not based on some basic drawings of random shapes drawn by them.

CMPT 275 Project: Health bars

🏆 HackWestern 1st Place (Data Visualization) - 🏥 Developed a Human Machine Interface though position tracking to accurately diagnose degenerative disorders such as Parkinson's. Built using Leap Motion hardware integrated with FFT ML algorithm with Python and displayed with Javascript and Angular 7.

MCPNet: EEG-Based Parkinson's Detection using Few-Shot Prototype Networks (Qiu et al. 2024). Clone + run setup.sh to auto-download real EEG data from OpenNeuro and run the full pipeline.

EEG-based Parkinson’s Disease detection using Graph Neural Networks (GCN) and MCPNet (CNN) pipelines.