### Presentation

```by:
Peter Hirschmann
Diagnosing Methods

Monitor symptoms
such as:

 Resting Tremor

 Rigidity
 Postural Instability
Sub-symptom
 Voice Problems
Use classification
teaching algorithms
to identify
Parkinson’s
Parkinson’s Disease

“Movement disorder
that is chronic and
progressive”
Parkinson's Disease Foundation


There is currently no
cure
Treatment involves
surgery or
medication
Parkinson Disease
Data – UCI Machine Learning
Repository










Status - Health status of the subject (one) - Parkinson's, (zero) - healthy
MDVP:Fo(Hz) - Average vocal fundamental frequency
MDVP:Fhi(Hz) - Maximum vocal fundamental frequency
MDVP:Flo(Hz) - Minimum vocal fundamental frequency
MDVP:Jitter(%),MDVP:Jitter(Abs),MDVP:RAP,MDVP:PPQ,Jitter:DDP – Several
measures of variation in fundamental frequency
MDVP:Shimmer,MDVP:Shimmer(dB),Shimmer:APQ3,Shimmer:APQ5,MDVP:
APQ,Shimmer:DDA - Several measures of variation in amplitude
NHR,HNR - Two measures of ratio of noise to tonal components in the voice
RPDE,D2 - Two nonlinear dynamical complexity measures
measures of fundamental frequency variation
Leave One Out – Useful for realistic testing, since all known data would be used for testing new patients.
Classification Methods
 Tries to fit the known data to a polynomial
model
 Provided the mean and variance, it bases
the identified vectors on a chosen model,
Gaussian, and attempts to fit model
parameters
 Searches and labels vectors based on the
closest identified vectors
Results – Polynomial Model


Sum Square Error

Training Error - Blue
Testing Error - Green
LOO Error - Red

Clearly, LOO has the lowest
Sum of Square Error
Features 1-22

Training Data
increases with
x-axis and
Testing Data
Decreases
Classification Rate
Results – Maximum Likelihood
Classification Rate
Samples: 1-185

Samples: 1-195
LOO testing
method
7 neighbors,
Classification Rate
vs. Percentage of
Data as Testing
Data
Percentage of Data used as
Testing Data 1%-95%
Classification Rate

Classification Rate
Results – Nearest Neighbor

# of Neighbors 1-7
LOO Method,
Classification Rate
vs. # of Neighbors
Summary
 This classification method only proves that single feature vectors
are not adequate but that LOO is best way to train
 Max Percentage when Training vs. Testing Data
= 0.8203
 Classification Rate using LOO
= 0.9330
Neighbors: 1
C_Rate:
1.0
2
0.8814
3
0.8763
4
0.8711
5
0.8711
6
0.8454
7
0.8247
 Best Classification Rate occurs when over 95% of the data is for
training for every amount of neighbors
 When using LOO, the best results occur from one nearest
neighbor
Conclusion
using
methodology with 1-7 neighbors are all
above 80%
 KNN is the best method of the three tested
 Still does not beat the comfort and
knowledge of a doctor, but highly useful as
a tool for mass testing. Would not require
a M.D. to eliminate people the majority of
people who do not have Parkinson’s
```