Parkinson Disease Prediction using Python

Abstract :

In the present decade of accelerated advances in Medical Sciences, most studies fail to lay focus on ageing diseases. These are diseases that display their symptoms at a much advanced stage and makes a complete recovery almost improbable. Parkinson’s disease (PD) is the second most commonly diagnosed neurodegenerative disorder of the brainwhere we delve into the exciting world of Parkinson’s disease (PD) and the advancements in technology that are revolutionizing its diagnosis and treatment. In this article, we will explore the fascinating field of machine learning and its potential in predicting the severity of PD using a technique called backpropagation. Latest  breakthroughs in Parkinson’s tele monitoring and the incredible possibilities it offers for early detection and improved patient care. The proposed system of python provide  PD prediction and discover the power of machine learning in transforming the lives of those affected by this neurodegenerative disorder. Parkinson Disease Prediction using Python

Parkinson Disease Prediction

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Parkinson Disease Prediction using Python

Software Requirements: –

• Operating System :    Windows OS
• Front End,Back-End:    Jupyter ,Python 3.10 ,
• Tool                     :    Miniconda3-latest-Windows-x86_64, Python 3.10

Hardware Requirements: –

• System                : Dual Core
• Hard Disk           : 40 GB
• Monitor              : 15 VGA Colour
•  Mouse                : Logitech
• RAM                   :  2 GB

System Modules:

1. User Give the input as dataset link from website url or local file dataset .
2. Calculating some statistical data like percentile, mean and std of the numerical values of the Series or DataFrame.
3. Process the Data Visualization as Graphical View
4. Measure the patients effected with Parkinson’s disease have high NHR that is the measures of ratio of noise to tonal components in the voice.
5. Measure RPDE is high in the patients effected with Parkinson’s disease.
6. Process the Distribution Plot
7. Splitting the Data as two category as X and Y.
8. Calculate the Model Accuracy by using Logistic Regression
9. Calculate the Model Accuracy by using Random Forest
10. Applying other machine learning models to see if there is improvement in accuracy.
11. Calculate the Model Accuracy by using Decision Tree
12. Calculate the Model Accuracy by using Naïve Bayes Classifier
13. Calculate the Model Accuracy by using K-NearestNeighbours
14. Calculate the Model Accuracy by using   SupportVectorMachine