This document discusses building a machine learning model to predict Parkinson's disease using voice recordings. It describes the dataset containing recordings from 80 subjects, with 40 having Parkinson's. 44 acoustic features were extracted from each recording. The model faces challenges from multicollinearity between correlated features and from the small replicated dataset. Various techniques are proposed to address these issues, including feature selection and engineering, dimensionality reduction, modeling techniques like neural networks, and constraining the model using a causal diagram. Evaluation on a separate test set aims for high sensitivity while balancing specificity and accuracy.

1. Prediction model from
Parkinson Dataset with
replicated features
Sermkiat Lolak, M.D.
Datascience for Healthcare, Department of Biostatistics and Clinical
Epidemiology,
Faculty of Medicine Ramathibodi Hospital, Mahidol University

2. Michale J. Fox

3. Source : Wikipedia

4. Symptoms
Gait Disturbance
Motor disabilities ,
Bradykinesia
depression, apathy,
and sleep disorder
Quality of Life

5. Early diagnosis —> Early Treatment
Medical
Surgical

6. Diagnosis
Clinical symptoms
Imaging
Laboratory
Biosensor signal

7. Biosensor signal
Motor related
Gait
Gesture
Voice

8. Voice
PD, Laryngeal / Diaphragm control can
produce vocal tremor
Vocal fold stiffness and bowing cause changes
in vocal fold mass and tension.
Fatigue across a prolonged voice loading task.
Not sustained phonation , Unstable
fundamental frequency (high jitter) and
amplitude (excess shimmer).

9. PD Voice Dataset
80 subjects,
40 of them
with PD
phonation
of /a/ vowel

10. 44 features were extracted from
each voice recording.
27 acoustic features
4 different characteristic families.
Features within each family are
highly correlated.

11. Replicated Data
Record 3 times
each subject ->
240 records
Multicollinearity
problem

12. Harmonic-to-Noise
Ratio (HNR)
Weak laryngeal control : incomplete
glottal closure.
Excess noise from unphonated air
leaks from the glottis.
This leads to lower HNR values.

13. MFCC
Derivatives Thirteen Mel Frequency
Cepstral Coefficients (MFCCs)
MFCCs : speech and speaker
recognition.
PD : problem with articulation
Low MFCC coefficients used for PD
diagnosis and tracking

14. IID & Statistical
Learning
Learning : Assumption of
Independent and Identical
distribution of data
Generalization
If not meeting assumption : Weaker
model

15. Learning
Machine Learning / Neural network :
Estimate conditional probability
(Lex Fridman / Judea Pearl)
Disentangled :
Entangled :
P(x1, x2, . . , xn) = ∏
n
i=1
P(xi |PA)
P(x1, x2, . . , xn) = ∏
n
i=1
P(xi |xi+1, . . , xn)
Bernhard Schölkopf (2019)

16. Problem of this
dataset
Time series
Highly correlated features

17. Correlated Voice
recording features
Delta 0 Delta 1 Delta 2
y
x1 x2 x3
y
Z
Common Cause Principle : Reichenbach (1956)

18. What to do?
Instrument variable : Z -> X -> Y
Mediator : X -> M -> Y
Reduce dimension :
Representative, Mean , PCA ,
Autoencoder
Modelling : eg. Neural network

19. Time series
Voice.
_t
PD_t PD_t+1
Voice
_t+1
Time series graph
HMM ?
Voice.
_t
Voice
_t+1
Base
line V.
Structural Causal
Diagram

20. What to do?
Model : RNN (LSTM , GRU) , CNN
Block information flow : Conditioning
Use only 1 session / person in training

21. Features
Engineering
Normalization :
Easier to
learn , esp. SVM
Use the mean
of the family
features

22. Features Selection
Random
Entropy
Gini

23. Experiments setting
Features engineering
Features selection
Model selection

24. Modelling
Linear : SVM
Tree based / Ensemble : Random Forest
Neural Network

25. Experiments
Seperate test set
Experiment each steps while fixing
other steps constant

26. Evaluation Metric
Early or
preemptive
diagnosis
Focus on high
Sensitivity (Recall)
AUC

27. Selected model
Neural Network 7 layers
Normalization , Representative
feature selection , Use only 1
session of patient (session 3) in
training

29. This model Naranjo et al*
Sensitivity /
Recall
1.0 0.825
Specificity 0.867 0.900
Precision 0.882 0.891
AUC 0.982 0.951
Accuracy 0.933 0.862
*Computer Methods and Programs in Biomedicine, 2017-04-01, Volume 142, Pages 147-156

30. Small dataset
Multicollinearity
Explicit Structural Causal Diagram
Advanced / mixed features engineering
and modelling
Discussion

31. Low specificity
Constrain: Orange
Hyperparameter tuning

32. Application
Transfer Learning
Application ,
training on mobile
phone voice
Acknowledgement
disease - go see
doctor