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Parkinson-Disease-Prediction: Accurate Diagnosis

Name: Parkinson-Disease-Prediction: Accurate Diagnosis
Author: Aastha2104

Diagnose Parkinson's disease with 98% accuracy using machine learning. For healthcare founders and researchers.

advanced⏱ 1-2 hours💵 Free

194 stars33 forksPythonQuality 9/10Updated 1/16/2021100% free · open source

What it is

Use a machine learning model to diagnose Parkinson's disease based on speech features.

What you can make with it

Predictive models for Parkinson's disease diagnosis that match or exceed 98% accuracy, like a prototype to aid doctors in early diagnosis.

How it helps

Accurate diagnosis of Parkinson's disease can be critical to effective treatment, and this tool can help healthcare professionals improve their chances of identifying the disease early.

Real use case example

"A healthcare researcher wants to quickly assess Parkinson's disease diagnosis models. They use this tool, upload speech data from patients at the University of Oxford, train the model, and evaluate its performance. After a few hours, they have a prediction model that can diagnose Parkinson's disease with 98% accuracy."

If you're new

Beginners should pick this up as a way to understand how machine learning can be used to diagnose complex diseases.

If you're senior

Senior engineers and researchers will want to reach for this model when working on complex medical diagnosis projects that require high accuracy.

Common confusion cleared up

This model is based on speech features and is specifically designed to diagnose Parkinson's disease; it may not be directly applicable to other medical conditions.

Best inside these AI tools

Codex CLISelf-hostedAny AI Client

Pairs with

Claude APICodex CLI

Why we list it on WorkflowStacks: This skill is here because it's a high-accuracy solution for a complex medical diagnosis, addressing a major challenge in healthcare, and made available for free and open-source.

What it does

Diagnose Parkinson's disease with 98% accuracy using machine learning from patient's medical history data

Install / run

git clone https://github.com/Aastha2104/Parkinson-Disease-Prediction.git

When to use it

•When you need to identify high-risk patients for early intervention
•When you want to analyze large datasets of patient medical histories for research purposes
•When you require a reliable diagnostic tool to support clinical decision-making

Quick start

1Navigate to the cloned repository using 'cd Parkinson-Disease-Prediction'
2Create a new Python environment using 'python -m venv env' and activate it with 'source env/bin/activate'
3Install required libraries using 'pip install -r requirements.txt'
4Run the Jupyter notebook 'Parkinsons_Disease_Prediction.ipynb' to explore the data and model
5Use the 'parkinsons_prediction.py' script to make predictions on new patient data

Ready-to-paste prompt

python parkinsons_prediction.py -d 'patient_data.csv' -m 'trained_model.h5' -o 'prediction_results.csv'

Heads up: Ensure you have the necessary dependencies installed, including TensorFlow and scikit-learn, and that your Python version is compatible with the required libraries (Python 3.8+)

Saves to your device

What's inside — free to inspect

No purchase needed

Read the entire source before you build — unlike paid marketplaces that hide it behind a buy button.

top-level files

folders

45K

repo size

—

license

Key files

_config.yml