mittrayash/Parkinson-s-Disease-Detection-using-Gait-Analysis
A research project that aims to detect Parkinson's disease in patients using Gait Analysis data. Subsequently, the project may make use of Gait Data Analysis to make powerful inferences which would help in genralizing the most common groups affected by this disease.
Leverages 8-sensor force plate data from the PhysioNet GaitPDB database, compressing 3M+ temporal measurements into statistical features (min, max, mean, median, std dev, skewness, kurtosis) for dimensionality reduction. Evaluates multiple classifiers—Logistic Regression, Decision Trees, Random Forest, SVM variants (linear/RBF/polynomial kernels), and k-NN—against PCA-based approaches to identify optimal detection performance.
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Feb 15, 2018
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