iBusiness, 2011, 3, 71-75
doi:10.4236/ib.2011.31012 Published Online March 2011 (http://www.SciRP.org/journal/ib)
A Data Mining Analysis of the Parkinson’s Disease
Shianghau Wu1, Jiannjong Guo2
1Faculty of Management and Administration, Macau University of Science and Technology, Macau, China; 2Graduate Institute of
Mainland China Studies, Tamkang University, Taiwan, China.
Received November 6th, 2010; revised December 25th, 2010; accepted January 4th, 2011.
ABSTRACT
Clinical decision-making needs available information to be the guidance for physicians. Nowadays, data mining
method is applied in medical research in order to analyze large volume of medical data. This study attempts to use data
mining method to analyze the databank of Parkinson’s disease and explore whether the voice measurement variables
can be the diagnostic tool for the Parkinson’s disease.
Keywords: Parkinson’s Disease, Data Mining, Decision Tree, Neural Network
1. Introduction
1.1. Medical Knowledge Management and Data
Mining
In clinical research, medical information is essential for
diagnosis and patient care. For clinical research, it also
provides useful information to facilitate therapeutic im-
provement and conduct medical researches. The medical
knowledge management in the realm of medical infor-
mation can be shown as the cycle among the clinical re-
search, guidelines, quality indicators, performance meas-
ures, outcomes and the concept [1]. In order to integrate
clinical information management, medical data analysis,
and application development, clinical decision intelli-
gence (CDI) is emerged in the new area to streamline the
data management from clinical practice, nursing, health-care
management, health-care administration. As for the CDI,
data mining is used in the knowledge acquisition and the
evidence-based research stage to analyze the information
extracted from research reports, reports, evidence tables,
flow charts, guidelines that include evidence contents,
sources and quality scores [2].
1.2. The Pa rk inso n’s D ise as e Ca se
The Parkinson’s disease (PD) is a type of neurological
disease. Many neurological diseases affect phonation of
patients, and voice can be a valuable aid in the diagnosis
of neurological disease [3,4]. In Parkinson’s disease,
voice disorders affect approximately 45% of patients [5].
Previous studies have shown that PD is associated with
vowel prolongation, syllable repetitions, isolated sentences
and conversation. The syllable repetitions (diadochokinesis,
DDK) are particularly useful for describing intensity de-
cay of language ability associated with PD [6]. Other
recent studies are related to the voice treatment of PD [7,
8]. Although previous studies offer some useful informa-
tion for PD diagnosis, whether voice measurements can
be the suitable tool for diagnosis needs to be examined.
This study aims at examining whether by solely using
vocal measurements can researchers discriminate PD pa-
tients from healthy people.
2. Method
The study applies several analysis methods, including
factor analysis, logistic regression, decision tree and
neural net, to analyze the dataset of PD. The goals of this
study include the following aspects,
1) Examine the biomedical voice measurements by
three data mining methods and find out which voice
measurement (and component) would significantly dis-
criminate PD patients from healthy people.
2) Examine the application of these data mining
methods to the PD dataset and find out which methods
would have the lowest Type 1 and Type 2 errors.
2.1. Data
This dataset, offered by Max Little of the University of
Oxford, in collaboration with the National Centre for
Voice and Speech, Denver, Colorado, who recorded the
speech signals, is composed of a range of biomedical
voice measurements from 31 people, 23 with Parkinson's
disease (PD). Each column in the table is a particular

A Data Mining Analysis of the Parkinson’s Disease 72
voice measure, and each row corresponds one of 195
voice recording from these individuals (“name” column).
The main aim of the data is to discriminate healthy peo-
ple from those with PD, according to “status” column
which is set to 0 for healthy and 1 for PD. The original
study published the feature extraction methods for gen-
eral voice disorders [9].
2.2. Factor Analysis
The goal of factor analysis is to find out the characteristics
of the variables in the databank. Factor analysis in multi-
variate techniques is used commonly. Factor analysis is a
mathematical tool which can be used to examine large
data set and utilize the entire correlation among variables
to find the communalities [10].
2.2.1. Factor Analysis Res ults
The study uses SPSS 10.0 software to analyze 22 voice
measurement variables (except status variable), and gets
the following results:
1) KMO Bartlett Test: KMO = 0.886 > 0.80. It means
there are communalities among variables, and it is suit-
able to conduct the factor analysis.
2) Communalities: The result of communalities is
listed below in Table 1.
3) The result of principal component analysis: In the
principal component analysis, the common method is the
eigen-value-greater-than-one rule and the scree plot. Ac-
cording to the result of principal component analysis and
the following scree plot, four components are retained.
The four components can explain 83.868% of the
Table 1. Communalities of voice measurement variables.
Variable Initial Extraction
V1 1.000 0.849
V2 1.000 0.499
V3 1.000 0.596
V4 1.000 0.984
V5 1.000 0.964
V6 1.000 0.984
V7 1.000 0.950
V8 1.000 0.984
V9 1.000 0.969
V10 1.000 0.967
V11 1.000 0.927
V12 1.000 0.968
V13 1.000 0.920
V14 1.000 0.927
NHR 1.000 0.895
HNR 1.000 0.814
RPDE 1.000 0.632
DFA 1.000 0.717
SPREAD1 1.000 0.839
SPREAD2 1.000 0.598
D2 1.000 0.649
PPE 1.000 0.821
Scree Plot
Component N umber
21191715131197531
Eigenv a lue
14
12
10
8
6
4
2
0
Figure 1. Scree plot of voice measurement variables.
the following scree plot, four components are retained.
The four components can explain 83.868% of the total
variance.
As for the principal component analysis, the orthogonal
rotation and varimax methods are widely used. The ob-
jective of rotation is to achieve a simpler factor structure
that can be meaningfully interpreted by the researcher.
An orthogonal rotation can be performed to achieve this
objective. In the orthogonal rotation, varimax and quarti-
max are most popular types, which the rotated factors are
orthogonal to each other [11]. The results of orthogonal
rotation and varimax methods are presented in Table 2,
the component 1 includes V9, V10, V11, V12, V13, V14,
HNR, D2. Because the component 1 is mainly composed
of variables about variation in amplitude, the component 1
can be renamed as the variation in amplitude. The com-
ponent 2 includes V4, V5, V6, V7, V8, NHR. Because
the component 2 is mainly composed of variables about
several measures of variation in fundamental frequency,
the component 2 can be renamed as the variation in fun-
damental frequency. The component 3 includes SPREAD1,
SPREAD2, V1, V3, RPDE, PPE. Because the component
3 is mainly composed of three nonlinear measures of
fundamental frequency variation, and V1, V3 are the
average and the minimum vocal fundamental frequency,
the component 3 can be renamed as the nonlinear meas-
ures of fundamental frequency variation. The component
4 includes DFA, V2. Because two variables have larger
difference, the component 4 can be renamed as the other
measure of voices.
2.2.2 Logistic Regression Results
The study attempts to examine whether physicians can
finely diagnose PD solely by means of the voice measure-
ments. Therefore, the study uses the logistic regression to
examine the odds of correct diagnosis of PD. The study
uses status variable as the dependent variable and the
component 1 to the component 4 as the covariates to con-

A Data Mining Analysis of the Parkinson’s Disease73
Table 2. The factor loadings after varimax rotating.
Component
1 2 3 4
V12 0.883 0.407 0.109 −0.103
V9 0.853 0.466 0.141 –6.741E–02
V13 0.837 0.435 0.168 –3.746E–02
V11 0.832 0.464 0.113 –7.707E–02
V14 0.832 0.464 0.113 –7.708E–02
V10 0.831 0.512 0.114 –4.642E–02
HNR –0.712 –0.437 –0.313 –0.131
D2 0.584 0.113 0.291 0.459
V8 0.397 0.900 0.112 6.270E–02
V6 0.398 0.900 0.112 6.270E–02
V4 0.409 0.887 0.167 4.099E–02
V5 0.279 0.873 0.327 –0.128
V7 0.483 0.835 0.134 –3.464E–02
NHR 0.386 0.822 0.125 0.233
V3 8.462E–02 –8.000E–02–0.753 0.122
RPDE 0.311 0.145 0.714 6.761E–02
SPREAD1 0.442 0.452 0.657 –8.951E–02
V1 0.158 –0.140 –0.642 0.626
SPREAD2 0.466 5.177E–02.613 4.635E–02
PPE 0.494 0.474 .578 –0.135
DFA 0.154 5.435E–02–1.714E–02 –0.831
V2 –3.008E–02 0.141 –0.126 0.680
struct the logistic regression model. The results are listed
below：
1) Cox & Snell R-square and Nagelkerke R-square: In
the logistic regression model, the Cox & Snell R-square
is 0.350 and the Nagelkerke R-square is 0.521. It means
these four components of voice measurement variables
have strong relationship with the health status variables.
2) Hosmer-Lemeshow test: The study uses the Hosmer
-Lemeshow test in order to examine whether the logistic
model is well fitted [12]. If the p-value of Hosmer-Leme–
show test is larger than 0.05, it means the model is well
fitted. In this logistic model, Chi-square value is 6.605,
p-value is 0.580 > 0.05. So the logistic regression model
is well fitted.
3) Classification: the logistic regression model offers
the prediction of classification. The classification result
is shown in Table 3.
In Table 3, 162 (= 26 + 136) cases are correctly clas-
sified into healthy groups and PD patients group, while
healthy cases are falsely classified into PD patients group,
and 11 PD cases are falsely classified into the healthy
group. The correct percentage of classification is 83.1%.
4) The result of the logistic regression: the coefficients
evaluation is presented in Table 4.
In Table 4, the significance ratio of the Wald Test in
Table 3. Classification result.
Predicted
Status Percentage Correct
0 1
Status 026 22 54.2
111 136 92.5
Overall Percentage 83.1
Table 4. The logistic regression result.
B S.E.Wald df Sig. Exp(B)
component1 1.8170.47214.823 1 0.000 6.152
component2 0.4240.4360.942 1 0.3321.527
component 31.4790.26531.099 1 0.000 4.389
component4 –0.5150.2285.114 1 0.024 0.597
Constant 2.1170.36533.557 1
0.000 8.308
component 2 (Variation in Fundamental Frequency) is
larger than 0.05. Other components, including component 1,
component 3, component 4, are significant (p < 0.05). So
component 1 (variation in amplitude), component 3
(nonlinear measures of fundamental frequency variation),
and component 4 (other measure of voices) are important
variables to predict and explain the healthy status.
According to the results of the logistic regression, the
odd ratio can be calculated from Table 4. The odd ratio
of component 1 is 6.152. It means when the component 1
increases 0.01 units, the probability of the odd between
PD cases and healthy cases increases 0.01 × (6.152 – 1) =
5.152%. In the same way, when the component 3 in-
creases 0.01 units, the probability of the odd between PD
cases and healthy cases increases 0.01 × (4.389 – 1) =
3.389%. Besides, when the component 4 increases 0.01
units, the probability of the odd between PD cases and
healthy cases increases 0.01 × (0.597 – 1) = –0.00403%.
2.3. Decision Tree Analysis
Decision tree analysis is useful for logical induction in
the data mining process. Decision tree induction is the
learning of decision trees from class-labeled training tu-
ples. A decision tree is a flowchart-like tree structure,
where each internal node represents a test on an attribute,
each branch represents an outcome of the test, and each
leaf node (terminal node) holds a class label. The top-
most node is the root node [13].
Rattle 2.4.78 software is applied to the decision tree
analysis in this study. The healthy status variable is the
response variable, and the 22 voice measurement variables
are the input variables in the decision tree model. In the
decision tree analysis, 70% of samples (136 cases) are
applied. The decision tree is shown in the Figure 2.
In Figure 2, the first node follows two decisions.
When the variable SPREAD 1 is less than or equal to

A Data Mining Analysis of the Parkinson’s Disease 74
Figure 2. Decision tree of the Parkinson’s disease case.
–6.324, 43 cases are classified into the healthy group
(status=0). If the variable SPREAD is larger than –6.324,
other unclassified cases would fall into the third node.
The third node follows two decisions. If the RPDE is less
than or equal to 0.398, 8 cases are classified into the spe-
cial group, while these members have 50% probability be-
long to the PD group or the healthy group. If the variable
RPDE is larger than 0.398, 85 cases are classified into
the PD group. The overall error probability of classifica-
tion is 8.47%.
2.4. Neural Net Analysis
A neural network is a set of connected input and output
units in which each connection has a weight associated
with it. During the learning phase, the network learns by
adjusting the weights so as to be able to predict the cor-
rect class label of the input tuples. Neural network learn-
ing is also referred to as connectionist learning due to the
connections between units [13]. The goal of the neural
net analysis is to build a model that is based on the idea
of multiple layers of neurons connected to each other,
feeding the numeric data through the network, combining
the numbers, to produce a final answer.
In the neural net model, 70% of the sample (136 cases)
is applied. According to the results calculated by Rattle
2.4.78 software, the neural net would be a 216–1–1 net-
work with 435 weights if considering the areas under the
ROC (Receiver Operating Characteristic) curves among
possible neural net models.
The error matrix of the neural net model is shown in
Table 5.
According to the result of calculation, the error prob-
ability of the classification is 23.73%.
Table 5. Error matrix for the Neural Net model (Percentage,
%).
SPREAD(P ＜ 0.001)
Predicted
Status
0 1
Status0 5 3
1 20 71
> －6.324
≦ －6.324
3. Discussion
RPDE
P = 0.003
The study applies the factor analysis, the logistic regression
method, the decision tree model and the neural net model
to analyze whether voice measurement variables can dis-
criminate PD patients from healthy people. The major
results are listed below,
N = 43
Status = 0
1) According to the results of the factor analysis and
the logistic regression model, the component 2 (Variation
in Fundamental Frequency) is insignificant. It represents
that jitter, the traditional measurement method evaluating
the extent of variation, doesn’t discriminate PD patients
significantly in the PD case. The noise-to-harmonics ra-
tios variables (NHR, HNR) are also belong to traditional
measurement variables [9]. In the factor analysis result,
NHR is one of the elements of the component 2, which is
insignificant in the logistic regression model. Therefore,
NHR is also insignificant. The result meets the result of
[9] in SVM classification performance results.
N = 8
Status = 0.5
N = 85
Status = 0
2) The result of the logistic regression model also in-
dicates the component 1(variation in amplitude) and the
component 3 (nonlinear measures of the fundamental
frequency variation) have the positive relationship with
the odd probability between the healthy group and the
PD patients group.
3) Little et al. (2008) indicate that vocal production is a
highly nonlinear dynamical system, and that changes
caused by impairments to the vocal organs, muscles and
nerves will affect the dynamics of the whole system. In
the nonlinear measurement variables, SPREAD1 and
RPDE are two important nodes in the decision tree model.
In the decision tree model, the value of SPREAD 1 is the
criterion to classify the healthy people in the whole sam-
ple. The value of RPDE can also be the criterion to clas-
sify the PD cases from the other members in the sample.
The result partially meets the conclusion of Little et al.
(2008), which estimates that PPE produces the best per-
formance in classification and the combination of HNR,
RPDE, DFA and PPE obtains best overall classification
performance.
4) Among all three methods, the decision tree model
has the lowest classification error probability, and the
logistic regression model is the second lowest, while the
neural net model has the highest classification error
probability.

A Data Mining Analysis of the Parkinson’s Disease 75
4. Conclusions
The study uses the data mining analysis to explore the
Parkinson’s Disease data. Data mining is widely used in
the realm of the preventive medicine. By means of the
study of the PD data, medical researchers can create the
evaluation table according to the results of data mining in
order to make physicians and ordinary people aware the
early symptoms of PD and make earlier treatments.
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