Parkinson-s-Disease-Detection-using-Gait-Analysis

Gait Analysis of Parkinson’s Disease (PD) Patients and Control Objects has been analyzed to show differences in PD Patients and Control Objects. Using data provided by Phisonet’s Gaitpdb database (in which 8 sensors have been applied to each foot of the subjects to calculate the Vertical Ground Reaction Forces (VGRF)), data compression has been performed using 7 statistical functions to get a representative image of the data. The statistical functions namely Minimum, Maximum, Mean, Median, Standard Deviation, Skewness, and Kurtosis have been used to compress over 3 million tuples into 310 tuples. Finally, various Machine Learning techniques have been applied to the transformed dataset to perform detection of Parkinson’s Disease. The classification has been performed using Logistic Regression, Decision Trees, Random Forest, SVM (Linear Kernel), SVM (RBF Kernel), SVM (Poly Kernel) and k-Nearest Neighbours.

Experiments with Principal Component Analysis for data compression have also been performed and their incompetence (with reasons) has been stated.

Name		Name	Last commit message	Last commit date
Latest commit History 123 Commits
.idea		.idea
.ipynb_checkpoints		.ipynb_checkpoints
CSV		CSV
Combined Datasets		Combined Datasets
PCA		PCA
PPT Material		PPT Material
Significant2d		Significant2d
Transformed Data		Transformed Data
__pycache__		__pycache__
img		img
.gitattributes		.gitattributes
.merged_datasets_random_forests.ipynb.swp		.merged_datasets_random_forests.ipynb.swp
Most_Prominent_Visual.ipynb		Most_Prominent_Visual.ipynb
Problem Statement.txt		Problem Statement.txt
README.md		README.md
Significant_Components_2d.ipynb		Significant_Components_2d.ipynb
Significant_components_3d.ipynb		Significant_components_3d.ipynb
Time Series Analysis.ipynb		Time Series Analysis.ipynb
adspy_shared_utilities.py		adspy_shared_utilities.py
data_vis.ipynb		data_vis.ipynb
master		master
merge_datasets.ipynb		merge_datasets.ipynb
merge_datasets_KNN.ipynb		merge_datasets_KNN.ipynb
merge_datasets_Logistic.ipynb		merge_datasets_Logistic.ipynb
merge_datasets_SVM.ipynb		merge_datasets_SVM.ipynb
merge_datasets_SVM_Linear.ipynb		merge_datasets_SVM_Linear.ipynb
merge_datasets_SVM_RBF.ipynb		merge_datasets_SVM_RBF.ipynb
merge_datasets_SVM_Sigmoid.ipynb		merge_datasets_SVM_Sigmoid.ipynb
merge_datasets_SVM_poly.ipynb		merge_datasets_SVM_poly.ipynb
merge_datasets_random_forests.ipynb		merge_datasets_random_forests.ipynb
merged_datasets_decision trees-checkpoint.ipynb		merged_datasets_decision trees-checkpoint.ipynb
merged_datasets_random_forests.ipynb		merged_datasets_random_forests.ipynb
merged_datasets_random_forests_with_pca.ipynb		merged_datasets_random_forests_with_pca.ipynb
pca_without_poly.ipynb		pca_without_poly.ipynb
polynomial_features_pca.ipynb		polynomial_features_pca.ipynb
random_forests.ipynb		random_forests.ipynb
svm.ipynb		svm.ipynb
svm_without_poly.ipynb		svm_without_poly.ipynb
svm_without_poly.py		svm_without_poly.py
svm_without_poly_scaled.ipynb		svm_without_poly_scaled.ipynb

mittrayash/Parkinson-s-Disease-Detection-using-Gait-Analysis

Folders and files

Latest commit

History

Repository files navigation

Parkinson-s-Disease-Detection-using-Gait-Analysis

About

Topics

Resources

Stars

Watchers

Forks

Languages