PREDICTION OF PARKINSON DISEASE USING AUTOENCODER
CONVOLUTIONAL NEURAL NETWORKS
Presented by
-Hema M S
-Maheshprabhu R
-Prema Arokia Mary G
-Nageswara Guptha M
-Aditi Sharma

CONTENT
• Introduction
• Problem Statement
• Solution
• Methdodology
• Conclusion
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INTRODUCTION
A disorder of the central nervous
system that affects movement,
often including tremors.
Nerve cell damage in the brain
causes dopamine levels to drop,
leading to the symptoms of
Parkinson's.
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PROBLEM STATEMENT

SOLUTION
5
 The predictions of PD in the early stage is important to improve the PD patients’ quality of
life.
 It can be done using a CNN with autoencoder feature extraction methodology.
Autoencoders CNN
Encoding
Decoding
Convolution Layer
Pooling Layer
Dense Layer

 The ReLU non-linear activation function was used in autoencoder.
 The cross entropy was used for error calculation.
 The Softmax function was used in the classification layer for classification.
 Convolutional neural network was proposed to classify PD patients using vocal data.
 The speech recording dataset from Oxford Parkinson diagnostic dataset was taken for
experimentation
 VGFR spectrogram detector and voice impairment classifier was used to classify PD patients.
From dataset using
PCA
From vocal dataset
using stacked
autoencoder

MACHINE LEARNING METHODS
 Recurrent Neural
Network (RNN) based
autoencoder such as long-
short term unit based
autoencoder and gated
recurrent based
autoencoder was used
 Grey wolf optimization
algorithm and meta
heuristic global search
optimization technique
and autoencoder were
proposed the extract
features from PD dataset
 Joint kernel based
feature selection method
was employed to select
the features from PD
 PD patients was classified
using Max-Margin
classification. SVM with
Gaussian kernel
methodology was used
for classification
 Artificial Neural
Network, classification
and regression tree and
SVM algorithms were
implemented for
classification
 A sparse feature
selection was used to
select the features from
the PD dataset.
 Fisher’s linear
discriminant analysis,
locality preserving
projection and least
square recursion model
were combined
 PD was predicted using
latent information
extraction technique. The
latent information was
extracted from deep
neural network that is
CNN
 The results showed that
logistic regression
multi-class classifier
produced better results
when compared to other 7

METHODOLOGY
The goal of the proposed architecture is to predict PD patients from the healthy
individuals.
 Data Collection
 Preprocessing
 Feature Extraction
 Classification
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Input
Data pre-
processing
Encode Decode
Feature Extraction – Autoencoder
Conv.
Layer
Pooling
Layer
Dense
layer
PD patients / Healthy People
Classification - CNN
Input
Data pre-
processing

RESULT
Split SVM CNN CNN – AE (proposed)
0.10 0.8234 0.9221 0.9434
0.15 0.8245 0.9389 0.9623
0.20 0.8211 0.9356 0.9444
0.30 0.8067 0.9241 0.9211
I. ACCURACY COMPARISON
 The dataset is taken from the PPMI database.

PRECISION COMPARISON OF ALL MODELS
Split SVM CNN
CNN – AE
(proposed)
0.10 0.7590 0.8601 0.9001
0.15 0.7680 0.8931 0.9143
0.20 0.7511 0.8723 0.9023
0.30 0.7423 0.8323 0.8921

I. RECALL COMPARISON OF ALL MODELS
Split SVM CNN
CNN – AE
(proposed)
0.10 0.7790 0.8801 0.9212
0.15 0.7810 0.8923 0.9363
0.20 0.7711 0.8790 0.9210
0.30 0.7412 0.8523 0.9112
SAMPLE FOOTER TEXT 20XX 11
I. MEASURE OF ALL MODELS
Split SVM CNN
CNN – AE
(proposed)
0.10 0.7680 0.8712 0.8934
0.15 0.7789 0.8831 0.9012
0.20 0.7681 0.8612 0.8901
0.30 0.7322 0.8412 0.8823

CONCLUSION
 The prediction of PD patients using CNN with autoencoder feature selection is proposed and
implemented.
 The autoencoder extracted essential features and noise is eliminated from the data.
 The convolutional neural network is used for classification.
 The CNN segmented the image and extracted the features automatically.
 The image is downsized using pooling layer.
 The classification is done in the dense layer of CNN. The PPMI data set taken for experimentation.
 The accuracy, precision, recall and f1-measured is considered for performance assessment.
 The result showed that the proposed methodology performed better when compared to other
methodologies.

THANK YOU