Machine Learning Approaches for Detection and Diagnosis of Parkinson’s Disease

YEAR : 2021

Description

This project attempts to evaluate the discriminative power of several predictors in the study to increase the efficiency of Parkinson’s disease detection through their symptoms. A number of classifiers including Support Vector Machine (SVM), Decision Tree, Neural Network, Random Forest classifier and Naïve Bayes (NB) are evaluated on a benchmark dataset obtained from Kaggle repository. It is observed that Neural Network achieved 100% accuracy.

ADDITIONAL INFORMATION

System Requirements

Operating System : Windows 7,8,10 (64 bit)
Software : Python
Tools : Anaconda (Jupyter notebook and anaconda prompt)

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