parkinsons-disease

Novoic's audio feature extraction library

A Folding@Home Docker container with GPU support

A research project that aims to detect Parkinson's disease in patients using Gait Analysis data. Subsequently, the project may make use of Gait Data Analysis to make powerful inferences which would help in genralizing the most common groups affected by this disease.

Cluster analysis of Parkinson's disease

Code for the paper "Vision-based Estimation of MDS-UPDRS Gait Scores for Assessing Parkinson’s Disease Motor Severity"

Sorce code of Apkinson: android app to monitor the motor symptoms of Parkinson's patients

Android app that tracks tremors and recommends follow up action

Analysis of Parkinson's Progressive Markers Initiative Data

Parkinson disease is associated with movement disorder symptoms, such as tremor, rigidity, bradykinesia, and postural instability. The manifestation of bradykinesia and rigidity is often in the early stages of the disease. These have a noticeable effect on the handwriting and sketching abilities of patients, and micrographia has been used for early-stage diagnosis of Parkinson’s disease. While handwriting of a person is influenced by a number of factors such as language proficiency and education, sketching of a shape such as the spiral has been found to be non-invasive and independent measure.

Parkinson's disease data analysis from uci machine learning repository dataset.

Parkinson's disease clinical study app

Spiro is an accessible, cheap, and portable diagnostic tool for the detection of Parkinson’s disease.

Detection of Degree of Parkinsonism via the Spiral Test

Parkinson's Progression Marker Initiative data science challenge, 2016

As an early diagnosis step machine learning classifiaction algorithms could be used in finding if the patient is prone to parkinsons disease.

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

Big Data Architecture of queues for real time video processing

This project was used for participating in the MPower Subchallenge of the Parkinson's disease digital biomarker DREAM challenge

early detection method for parkinson's disease using deep ensemble learning on MRI dataset

A regularized logistic regression model built from scratch which is used to predict whether a patient is suffering from Parkinson's disease or is healthy.

In this Python machine learning project, using the Python libraries scikit-learn, numpy, pandas, and xgboost, I have build a model using an XGBClassifier. We’ll load the data, get the features and labels, scale the features, then split the dataset, build an XGBClassifier, and then calculate the accuracy of our model.

Rmarkdown scripts for adult human postmortem SN & Cortex cell atlas project.

App to Detect Parkinson's Disease

Computer Intelligence subject final project at UPC.

Using 2D and 3D AUC diagrams we provide improved detection accuracy of Parkinson's disease

To implement an Ensemble Classifier of Logistic regression, Naive Bayes & Decision Tree. The goal is to implement a classification model to predict the “Status” feature in the provided Parkinson dataset.

Codes for "Parkinson’s Disease Diagnosis: Effect of Autoencoders to Extract Features from Vocal Characteristics"