Parkinson-Disease-Prediction-in-Early-Stages

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The Parkinson disease contribute up to 45% of sudden deaths. So it is very important to detect or predict before it reaches to serious stages. If Parkinson predicted in its early stages, then it helps to save the lives. Statistical methods are generally used for classification of risks of Parkinson i.e. high risk or low risk. Sometime it becomes difficult to handle the complex interactions of highdimensional data.

Parkinson's disease is a progressive nervous system disorder that affects movement. Symptoms start gradually, sometimes starting with a barely noticeable tremor in just one hand. Tremors are common, but the disorder also commonly causes stiffness or slowing of movement.

In the early stages of Parkinson's disease, your face may show little or no expression. Your arms may not swing when you walk. Your speech may become soft or slurred. Parkinson's disease symptoms worsen as your condition progresses over time.

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Objectives

Machine learning techniques can be used to overcome these drawbacks which are cause due to the high dimensions of the data. So in this project I am using machine learning algorithms to predict the chances of getting cancer.

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Installation

Copy and Run this in terminal:

pip3 install -r requirements.txt

git clone https://github.com/kanishksh4rma/Parkinson-Disease-Prediction-in-Early-Stages.git

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Libraries used :

  * pandas
  * numpy
  * matplotlib
  * seaborn
  * sklearn
  * keras

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Algorithms Used

The Algorithms used are :

* MultiLayerPerceptrons (MLP) in Deep Learning
* RandomForestClassifier

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Accuracy :

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Developed by: Kanishk Sharma