Journal of Software Engineering and Applications, 2011, 4, 388-390
doi:10.4236/jsea.2011.46044 Published Online June 2011 (http://www.SciRP.org/journal/jsea)
Copyright © 2011 SciRes. JSEA
Prediction of Outcome in the Vegetative State by
Machine Learning Algorithms: A Model for
Loris Pignolo1*, Vincenzo Lagani2
1S. Anna Rehabilitation Institute and RAN-Research on Advanced Neuro-rehabilitation, Crotone, Italy; 2Biomedical Laboratory,
Institute of Computer Science, Foundation for Research and Techno l ogy-Hellas, Heraklion, Greece.
Email: email@example.com, firstname.lastname@example.org
Received June 8th, 2010; revised March 13th, 2011; accepted April 20th, 2011.
Purpose of this study was to compare different Machine Learning classifiers (C4.5, Support Vector Machine, Naive
Bayes, K-NN) in the early prediction of outcome of the subjects in vegetative state due to traumatic brain injury. Accu-
racy proved acceptable for all compared methods (AUC > 0.8), but sensitivity and specificity varied considerably and
only some classifiers (in particular, Supp ort Vector Machine) appear applicable models in the clinical routine. A com-
bined use of classifiers is advisable.
Keywords: Artificial Intelligence, Vegetative State, Clinical Outcome, Prognosis
Vegetative state (VS) is a clinical condition resulting
from severe brain damage and characterized by the lack
of awareness (of self and environment), voluntary or
otherwise purposeful behavioral responses, and commu-
nication in patients [1-6]. The early prediction of out-
come has high priority for both the patient’s family and
attending physicians, who need to plan dedication and
resources [7-9]. However, no prognostic model has yet
proved suitable of generalization across different set-
tings. Neither the traditional or newly developed elec-
trophysiological techniques (waking and sleep EEG,
visual auditory or somatosensory evoked potentials,
event-related potentials, EEG brain imaging) nor other
methods of functional evaluation (fMRI) are usable
prognostic tools. An empirical prognostic model has
been developed based on the appearance/disappearance
and timing of observation of conventional neurological
signs [10,11] and made available to clinicians. Eye
tracking (i.e. smooth eye pursuit of moving target),
spontaneous motility, the oculocephalic reflex and the
chewing/sucking reflex were the relevant signs ac-
cording to the model developed by data mining algo-
rithms, with greater accuracy of prediction in post-
traumatic subjects in VS. Prognosis nevertheless de-
pended on evolution over time rather than on early as-
Purpose of this study is to assess the efficacy of
theavailable decisional models in comparison with
those suitable of application in this context. To this end ,
a subpopulation of subjects in VS due to head trauma
was studied at admittance to the dedicated unit for VS
of the Institute S. Anna – RAN.
Two hundreds forty one patients admitted to the dedi-
cated semi-intensive care over an 11-year period (April
1998 - June 2009) were considered retrospectively. In-
clusion criteria were posttraumatic aetiology and a diag-
nosis of vegetative state, as clinically defined compliant
to the criteria suggested by the Multi-Society Task Force
and the guidelines of the London Consensus Conference
[5,12]. Patients recovering consciousness within 4 weeks
from brain injury, with severe spinal fractures spinal cord
damage or otherwise requiring intensive treatment or sur-
gery, or in VS resulting from alcohol or drug overdose,
or with a Glasgow Coma Scale  rating > 8 at admis-
sion were excluded in order to minimize misdiagnoses.
For each patient the following parameters were col-
lected: age, sex, days in hospital reanimation unit, Gas-
cow Coma Scale at admission, necessity of tracheotomy,
necessity of nasogastric feeding tube (NFT), need of per-
Prediction of Outcome in the Vegetative State by Machine Learning Algorithms: A Model for Clinicians?389
cutaneous endoscopic gastronomy (PEG), presence of
dysautonomic syndrome. The presence of preselected
neurological signs (spontaneous motility, chewing and
sucking reflexes, oculo-cephalic reflex, eye tracking) was
searched for at admission. The outcome were assessed by
the Glasgow Outcome Scale (GOS) ; patients were
allocated to two main clusters of classes, i.e. to negative
(GOS 1 or 2) or positive (GOS score 3 to 5) outcome.
All the numerical variables were normalized in the in-
terval (0,1). Four different machine learning methods
were used for data analysis: K Nearest Neighbours
(K-NN) , Naïve Bayes , C4.5 Decision Tree
[17,18], Support Vector Machine (SVM) . The se-
lected algorithms follow distinct approaches in model
development. A major methodological issue was to
compare the suitability of a decision tree rule-based ap-
proach (C4.5), Bayesian methods or numerical algo-
rithms (SVM, K-NN ) [2 0].
These algorithms require the “a priori” specification of
one or more parameters . We used a repeated cross
validation procedure to evaluate the results of each pa-
rameter configuration and avoid over-fitting. Cross vali-
dation is an established technique for the assessment of
the generalizability of the result of a statistical analysis
. It partitions the dataset in several complementary
folds, to then use each fold as test-set, while the remain-
ing dataset is used to build a model/execute the analysis.
The results of the different test-sets are then averaged.
The repetition of dataset splitting guarantees the results
independence from the actual dataset subdivision .
As a further step, we used the Area Under the Curve
(AUC) [24,25] metrics to identify the best configuration
of parameters for each machine learning algorithm. AUC
values can range from 0 (total misclassification) to 1
(perfect classification); random classification is arounf
0.5. AUC has the advantage of independency from distri-
bution, i.e. evaluation of the results is not biase in over-
represented classes. In addition, we estimated for each
algorithm the sensitivity and specificity for positive out-
come . All the experiments were performed making
use of the WEKA (Waikato Environment for Knowledge
Analysis) software [27,28].
3. Results and Discussion
AUC values indicate good classification performances
for all the used classifiers; however, the sensitivity and
specificity estimates outline differences among the used
statistical methods (Tables 1 and 2). Sensitivity was al-
ways higher than specificity, therefore indicating that the
methods detect positive outcomes more easily (i.e. posi-
tive outcome can be excluded for patients classified as
candidates to a negative outcome; on the contrary, low
specificity does not exclude a negative prognosis for pa-
Table 1. AUC for each dataset and algorithm.
Naive Bayes 0.91
Table 2. Sensitivity and specificity for class “positive out-
com e” (GOS values 3, 4 and 5).
C4.5 0.94 0.58
SVM 0.97 0.65
Naive Bayes 0.83 0.77
K-NN 0.97 0.44
tients whose outcome was classified as positive); high
sensitivity of the machine learning model is therefore a
prerequisite in the early identification of patients with
positive prognosis. In this respect, SVM seems to be the
most suitable models for performing differential prog-
nostic evaluations. On the contrary, Naïve Bayes sensi-
tivity and specificity values are similar, indicating that
such models can identify both the positive and negative
outcomes with acceptable accuracy. C4.5 and K-NN
methods proved Pareto dominated  by the other two
We compared four different machine learning methods
(C4.5, SVM, Naïve Bayes and K-NN) to identify the
most suitable algorithm in the prognostic evaluation of
subjects in vegetative state. All the tested algorithms are
usable in this respect. SVM can be used for differential
prognostic evaluations, i.e. SVM models may be a useful
clinical tool to exclude a positive outcome. K-NN and
C4.5 could be used for the same purpose, but their sensi-
tivity and specificity are inferior to SVM. The Naïve
Bayes classifiers do not appear usable for differential
prognosis, due to poor efficiency in recognizing a spe-
cific class of subjects, but have limited classification er-
rors and can be still considered as a valid (ancillary)
prognostic tool. A decision to use classifiers specialized
in differential prognosis or predictiv e models able to g ive
reasonably accurate evaluation for both classes would
depend on the clinical rationale and applied protocols. It
may be worth noting that C4.5 remains a tool with poten-
tial clinical application in spite of poor performances; it
is the only algorithm among those studied to be able to
provide graphical models user-friendly to the clinician.
The ccombined use of different machine learning tools
may be preferable in the vegetative state clinical setting,
Copyright © 2011 SciRes. JSEA
Prediction of Outcome in the Vegetative State by Machine Learning Algorithms: A Model for Clinicians?
Copyright © 2011 SciRes. JSEA
with the approach complexity predictably compensated
for by the overall resources that the care of patients in
vegetative state requires.
 B. Jennett and F. Plum, “Persistent Vegetative State after
Brain Damage: A Syndrome in Search of a Name,” Lan-
cet, Vol. 1, 1972, pp. 734-619.
 G. Dolce and L. Sazbon, “The Posttraumatic Vegetative
state,” Stuttgart, Thiene, 2002.
 S. Laureys, “The Neural Correlate of (un) Awareness:
Lessons from the Vegetative State,” Trends in Cognitive
Sciences, Vol. 9, No. 12, 2005, pp. 556-559.
 B. Jennett, “The Vegetative State,” Cambridge University
Press, Cambridge, 2002.
 Multi-Society Task Force on PVS, “Statement on Medi-
cal Aspects of the Persistent Vegetative State,” The New
England Journal of Medicine, Vol. 330, No. 21, 1994, pp.
 A. Zeman, “Consciousness,” Brain, Vol. 124, 2001, pp.
 R. Braakman, W. B. Jennett and J. M. Minderhoud,
“Prognosis of the Post Traumatic Vegetative State,” Acta
Neurologica Scandinavica, Vol. 95, 1988, pp. 49-52.
 E. Schmutzard, A. Kampfl, G. Franz, B. Pfausler, H. P.
Haring, H. Ulmer, S. Felber, S. Golaszewski and F.
Aichner, “Prediction of Recovery from Post Traumatic
Vegetative State with Cerebral Magnetic-Resonance Im-
aging,” Lancet, Vol. 351, No. 9118, 1998, pp. 1663-1671.
 A. Rovlias and S. Kotsou, “Classification and Regression
Tree for Prediction of Outcome after Severe Head Injury
Using Simple Clinical and Laboratory Variables,” Jour-
nal of Neurotrauma, Vol. 21, 2004, pp. 886-893.
 G. Dolce, M. Quintieri, S. Serra, V. Lagani and L.
Pignolo, “Clinical Signs and Early Prognosis: A Deci-
sional Tree, Data Mining Study,” Brain Injury, Vol. 22,
No. 7-8, 2008, pp. 617-623.
 L. Pignolo, M. Quintieri and W. G. Sannita, “The Glas-
gow Outcome Scale in Vegetative State: A Possible
Source of Bias,” Brain Injury, Vol. 23, No. 1, 2009, pp.
 K. Andrews (Chairman), “International Working Party
Report on the Vegetative State,” Royal Hospital for
 G. Teasdale and B. Jennet, “Assessment of Coma and
Impaired Consciousness: A Practical Scale,” Lancet, Vol.
2, 1974, pp. 81-84.doi:10.1016/S0140-6736(74)91639-0
 B. Jennet and M. Bond, “Assessment Outcome after Se-
vere Brain Damage: A Practical Scale,” Lancet, Vol. 1,
1976, pp. 480-484.
 G. Shakhnarovish, T. Darrell and P. Indyk, “Near-
est-Neighbor Methods in Learning and Vision,” The MIT
Press, Cambridge, 2005.
 P. Domingos and M. Pazzani, “On the Optimality of the
Simple Bayesian Classifier under Zero-One Loss,” Ma-
chine Learning, Vol. 29, 1997, pp. 103-137.
 J. R. Quinlan, “C4.5: Programs for Machine Learning,”
Morgan Kaufmann Publishers, Waltham, 1993.
 L. Breiman, J. H. Friedman and R. A. Olshen, “Classifi-
cation and Regression Trees,” Wadsworth International,
Belmont, CA, 1984.
 N. Cristianini and J. Shawe-Taylor, “An Introduction to
Support Vector Machines and Other Kernel-Based
Learning Methods,” Cambridge University Press, Cam-
 R. C. Holte, “Very Simple Classification Rules Perform
Well on Most Commonly Used Datasets,” Machine
Learning, Vol. 11, No. 1, 1993, pp. 63-90.
 F. Moser, G. E. Rong and E. Martin, “Joint Cluster
Analysis of Attribute and Relationship Data without Ap-
riori Specification of the Number of Clusters,” In Pro-
ceedings of the 13th ACM SIGKDD International Con-
ference on Know- ledge Discovery and Data Mining, San
Jose, California, 12-15 August 2007.
 M. Stone, “Cross-Validatory Choice and Assessment of
Statistical Predictions,” Journal of the Royal Statistical
Society, Vol. 36, No. 2, 1974, pp. 111-147.
 R. Picard and D. Cook, “Cross-Validation of Regression
Models,” Journal of the American Statistical Association,
Vol. 79, No. 387, 1984, pp. 575-583.
 M. L. Thompson and W. Zucchini, “On the Statistical
Analysis of ROC Curves,” Statistics in Medicine, Vol. 8,
No. 10, 1989, pp. 1277-1290.
 M. S, Pepe, “A Regression Modelling Framework for
ROC Curves in Medical Diagnostic Testing,” Biometrika,
Vol. 84, 1998, pp. 595-608.
 R. W. Thatcher, R. A. Walker, C. J. Biver, D. M. North
and R. Curtin, “Sensitivity and Specificity of an EEG
Normative Database: Validation and Clinical Correla-
tion,” Journal of Neurotherapy, Vol. 7, No. 3-4, 2003, pp.
 E. Frank, “Machine Learning with WEKA,” University of
Waikato, New Zealand, 1999.
 J. H. Van Bemmel and M. A. Munsen, “Handbook of
Medical Informatics,” Springer-Verlag, Berlin, 1997.
 M. J. Osborne and A. Rubenstein, “A Course in Game
Theory,” MIT Press, Cambridge, 1994.