Machine learning and GDV images: Diagnosis and therapy verification
Igor Kononenko, Tatjana Zrimec, Bor Prihavec, Matjaž Bevk, Slobodan Stanojević
University of Ljubljana,
Faculty of Computer and Information Science, Tržaška 25
SI-1001 Ljubljana, Slovenia
Center za svetovanje in pomoč OM-AN, Dunajska 270, Ljubljana
tel: +386-61-1768390, fax: +386-61-1264647
e-mail: {igor.kononenko;tatjana.zrimec}@fri.uni-lj.si
Abstract
Machine learning technology is well suited for the induction of diagnostic and prognostic rules and
solving of small and specialized classification, diagnostic and prognostic problems. Recently
developed technology for recording the human bioelectric field using the Gas Discharge
Visualisation (GDV) technique provides possibly useful information about the biophysical and/or
psychical state of the object/patient. We use machine learning to interpret the GDV coronas of
human's fingers in order to verify three hypotheses: 1. The GDV images contain useful information
about the patient, 2. The map of coronas of fingers according to Chinese medicine does make sense,
and 3. The extrasense healer is able to see by himself (with his natural senses) the energetic disorders
in the human body.
Introduction
Machine learning technology (e.g. Mitchell, 1997; Kononenko 1993; Kononenko et al., 1998;
Dietterich and Shavlik, 1990) is well suited for the induction of diagnostic and prognostic rules and
solving of small and specialized classification, diagnostic and prognostic problems. The classification
knowledge can be automatically derived from the description of cases solved in the past.
Recently developed technology for recording human's bioelectric field by using the Gas Discharge
Visualisation (GDV) technique (Korotkov, 1998) provide a possibly useful information about the
biophysical and psychical state of the object/patient that could in some cases improve the
classification/diagnostic process. However, the problem is the interpretation of the GDV images. By
using machine learning we could alleviate that problem by means of automatically generating
classification rules from GDV images. For that purpose the Kirlian images need to be described with
a set of parameters.
Korotkov's team has developed a computer program that generates the corona of the whole human's
body from coronas of all ten fingertips. The program is based on a map, known from traditional
Chinese medicine and described in Mandel's book (1986). This map defines regions (sectors) of each
finger's corona to be related with specific organ or organ system in the body. Korotkov (1998) and
his team slightly modified this map.
We are using the Crown-TV to establish the relation between the patient's energetic state as seen by
an extrasense therapist by himself (with his natural senses) and the coronas of fingers of that person.
The additional benefit of Crown-TV is to determine the effect of the bioenergetical or any other
therapy to the energetic state of the patient.
Therefore the study tries to verify three hypotheses at once (of course it would be better to deal with
one hypothesis at a time but we were not able to do that yet):

The corona contains useful information for diagnosis.

The map of the fingers' coronas makes sense - the particular sectors corresponding to a specific
organ contain useful information about the state of that organ.

The extrasense therapist does see the energetic state of the patient and is able to diagnose patient
with this information.
Kirlian effect for studying the object's/human's bioelectric field
All over the world various researchers study unusual phenomena, most of them under the name of
parapsychology, and probably the most experienced researchers are in Russia (Lee, 1996). Most widely
are studied telephaty, telekinetics, and extrasensory perception. Quite some people are known that
are able to see the bioelectric field, also known as aura. With the development of technology it has
become possible to scientifically study some aspects of the bioelectric field phenomenon.
Korotkov (1998) and his team in St. Petersburg, Russia developed the instrument, Crown-TV that
can be routinely used. This opens practical possibilities to study the effects of GDV. Besides studying
non-living objects, such as water, various liquids, and minerals, the most widely studied are living
organisms: plants (leafs, seeds, etc.), animals, and of course humans. For humans, most widely are
recorded bioelectric fields of fingers, and GDV records of blood excerpts. Principal among these are
studies of the psycho-physiological state and energy of the human being, diagnosis, reactions to some
medicines, reactions to various substances, food, dental treatment, alternative healing treatment, such
as acupuncture, ‘bioenergy’, homeopathy, various relaxation and massage techniques, GEM therapy,
applied kineziology and flower essence treatment, leech therapy, and even studying the GDV images
after death.
An important part of the image analysis is to formulate and describe visual information, such as what
type of image features we can extract from the images, what properties those features are expected to
have, and how they are related to each other. However, a lot of knowledge is required to read
bioelectric field. The image analysis of those images requires preprocessing, segmentation into
meaningful image structures and transformation of the structures into a suitable representation. In
our study we used the GDV Analysis software developed by Korotkov's team and which is provided
together with the Crown-TV equipment. For each finger the program outputs up to 70 numeric
parameters (attributes), altogether 610 attributes. The attributes that are used to describe the GDV
images are:
1. Area of GDV-gram.
2. Noise, deleted from the picture (depends on the first setting in the program).
3. Form coefficient K = L2/2S.
4. Fractal dimension.
5, 6. Brightness coefficient and deviation.
7. Number of separated fragments in the image.
8, 9. Average area of the fragments and its deviation.
10. Form coefficient II (ratio) KII = (Simage + Soval )/Soval.
11. Relative coefficient of glow inside the inner oval.
12-15. Relative coefficient of image glow for 25, 50, 75 and 100% area (from the whole S)
16. Number of sectors (varies from 6 to 9).
17. Areas in the sectors (17.1; 17.2; up to 17.9).
18. Relative coefficients for the sectors (18.1.1 up to 18.9.4).
The measurements and the results
We recorded the coronas of all ten fingers of patients on their first visit to the extrasense healer and
we taped the observations made by the healer on the audio tapes. The observations were made for all
organs of the whole body. The ‘energetic diagnosis’ for one patient contains the description of the
state for 65 organs/parts of organs/glands (e.g. small brain, stomach, left and right kidney, thyroid
gland, etc.), 8 parts of the body (e.g. arms, head, neck, legs etc.), 8 physical/psychical functions (e.g.
respiration, digestion, concentration, sleeping etc.), and 17 possible diagnoses in terms of classical
medicine (e.g. rheumatism, cold, headache, hemorrhoids etc.). For each organ and part of the body
the state can be either OK, energetic blockage, strong energetic blockage, incorrect function, no
function or damaged.
Off-line the ‘diagnoses’ were input into the computer in terms of a matrix of possible energetic
disorders. Due to technical reasons we lost half of our recordings and till now we have the data for
only 60 patients that is useful for our study. Due to small number of cases we decided for preliminary
investigation to process only one energetic disorder: the energetic blockage or other dysfunction of the heart.
As the number of training instances is low, we have to limit the number of attributes. Namely with
altogether 610 attributes and only 60 cases there is a high probability that certain irrelevant attributes
will seem to be very relevant just by chance. We performed several experiments with different
number of attributes:
1. In the first experiment with only 52 patients (8 with diagnosis ‘incorrect functioning of the heart’
were excluded in this experiment) we selected 4 attributes describing the areas of coronas' sectors
that correspond to heart (one sector of each middle finger and one sector of each little finger).
2. For the second experiment we selected all (92) attributes for both middle and both little fingers.
3. We selected the attributes describing the areas of coronas' sectors that correspond to organs or
organic systems related to heart: heart (4 sectors of middle and little fingers), blood circulation (2
sectors of middle fingers), thorax zone (2 sectors of middle fingers), spleen (2 sectors of ring
fingers), respiratory system (2 sectors of little fingers), jejunum (2 sectors of little finger) and
coronary vessels (2 sectors of little finger). Altogether 16 attributes.
4. We selected 19 attributes as estimated the best among all 610 attributes by statistical criteria
(ReliefF, Gini index, Information gain, Gain-ratio, Accuracy, and MDL (Kononenko, 1995)). Of
course, due to high number of attributes and small number of cases we may expect that some
irrelevant attributes will by statistical criteria seem important. However, an interesting result that
supports the three hypotheses, is that this set of 19 best attributes contains all 4 sectors
corresponding to heart. The probability that they would be selected by chance out from 610
attributes in 19 trials is less than 10-6.
We used the C4.5 machine learning algorithm (Quinlan, 1993) that builds decision trees. In all
experiments we performed a 5-fold cross validation on our 60 examples that were described with 4,
92, 16, and 19 attributes and classified into two classes: problems with heart (36 cases) and no
problems with heart (24 cases). Note that the error of a default classifier (that classifies all instances
into the majority class) would be 40% and the error of the probabilistic classifier would be 48%. The
results are provided in Table 1.
These results are interesting; however, due to low number of cases they are not very reliable. The
results show that the attributes describing the areas of coronas' sectors that correspond to heart are
indeed most relevant. By adding other attributes the error increases. The worst situation is when we
leave the selection to statistical measures (last experiment with 19 attributes) which was in fact
expected, as the number of cases is significantly lower than the number of all attributes.
Experiment
1
2
3
4
# attributes
4
92
16
19
error (%)
32.7  4.7
35.0  6.1
38.3  4.2
45.0  5.7
Table 1 Average classification error with standard deviation (5-fold cross validation on 60
cases from 2 classes) in experiments with different number of attributes
Conclusions and further work
By using machine learning algorithms we can use the numeric description of coronas to relate them
with diagnosis, prediction, therapy, time etc. Current results are encouraging. Although the number
of cases is currently too small for reliable conclusions the results indicate that there is a correlation
between the ‘energetic diagnosis’ of heart made by the extrasense healer by himself and the sectors of
coronas of fingers, that are by Chinese medicine related with heart. This was shown by our first
experiment as well as by the statistical estimation of the quality of the attributes. In the first
experiment the classification error is 32.7  4.7%, which is better than default classifier (40%) and
much better than probabilistic classifier (48%).
The statistical estimation selected in a set of 19 best attributes out of 610 attribute all 4 attributes that
represent areas of coronas that correspond by Chinese medicine to heart. If the diagnoses were pure
random (no significant information from the extrasense healer) and if the Chinese medicine was
wrong and the Kirlian camera exhibited only noisy coronas (no significant information from any
attribute) than all attributes would contain only noise. If this would be the case the probability that
the statistical estimation would select all 4 attributes by chance out from 610 attributes in 19 trials is
less than 10-6.
We plan to collect more cases and to perform similar experiments also for other organs/organ
systems. If it is possible we will try to relate the GDV records of patients with diagnoses which were
confirmed by physicians. In that case, we would eliminate one hypothesis (that the extrasense healer
really sees relevant information) and would be able to focus on the information provided by the
Kirlian camera. On the other hand it would be very interesting to relate the observations made by the
extrasense healer with the observations of physicians.
We plan also to use the Crown-TV to follow the effect of the healing therapy on the energetic state
of the patient.
References
I.Kononenko (1993) Inductive and Bayesian learning in medical diagnosis, Applied Artificial Intelligence, 7:317337.
I.Kononenko (1995) On Biases in Estimating Multivalued Attributes, Proc. Int. Joint Conf. on Artificial Intelligence
IJCAI-95, Montreal, August 20-25 1995, pp. 1034-1040.
I.Kononenko, I.Bratko, M.Kukar (1998) Application of machine learning to medical diagnosis. In:
R.S.Michalski, I.Bratko, and M.Kubat (eds.): Machine Learning, Data Mining and Knowledge Discovery: Methods and
Applications, John Wiley & Sons.
K.Korotkov (1998) Aura and Consciousness: A New Stage of Scientific Understanding, St.Petersburg, Russia: State
Editing & Publishing Unit “Kultura”.
A.G.Lee (1996) The Selected Works in the Region of Parapsychology, Moscow, Russia: L.L.Vasilyev Fund of
Parapsychology.
P.Mandel (1986) Energy Emission Analysis, Synthesis Publ. Comp.
T.Mitchell (1997) Machine Learning, McGraw Hill.
Quinlan J.R. (1993) C4.5 programs for machine learning, Morgan Kaufmann.