Machine learning for medical diagnosis: history, state of the art and perspective
Igor Kononenko ,
Faculty of Computer and Information Science, University of Ljubljana, Tr a ka 25, 1001 Ljubljana, Slovenia
Abstract
The paper provides an overview of the development of intelligent data analysis in medicine from a machine learning perspective: a historical view, a state-of-the-art view, and a view on some future trends in this subfield of applied artificial intelligence. The paper is not intended to provide a comprehensive overview but rather describes some subareas and directions which from my personal point of view seem to be important for applying machine learning in medical diagnosis. In the historical overview, I emphasize the naive Bayesian classifier, neural networks and decision trees. I present a comparison of some state-of-the-art systems, representatives from each branch of machine learning, when applied to several medical diagnostic tasks. The future trends are illustrated by two case studies. The first describes a recently developed method for dealing with reliability of decisions of classifiers, which seems to be promising for intelligent data analysis in medicine. The second describes an approach to using machine learning in order to verify some unexplained phenomena from complementary medicine, which is not (yet) approved by the orthodox medical community but could in the future play an important role in overall medical diagnosis and treatment.
Author Keywords: Machine learning; Medical diagnosis; Reliability of prediction; Kirlian camera
I copied and pasted the most important part of the paper...
4.2. Machine learning in complementary medicine
4.2.1. Kirlian effect — a scientific tool for studying subtle energies
The history of the so-called Kirlian effect, also known as the gas discharge visualization (GDV) technique (a wider term that includes also some other techniques is bioelectrography), goes back to 1777 when G.C. Lihtenberg in Germany recorded electrographs of sliding discharge in dust created by static electricity and electric sparks. Later, various researches contributed to the development of the technique [66]: Nikola Tesla in the USA, J.J. Narkiewich-Jodko in Russia, Pratt and Schiemmer in Prague until the Russian technician Semyon D. Kirlian together with his wife Valentina noticed that through the interaction of electric currents and photograph plates, imprints of living organisms developed on film. In 1970, hundreds of enthusiasts started to reproduce Kirlian photos and the research was until 1995 limited to using a photo-paper technique. In 1995, a new approach based on CCD video techniques and computer processing of data was developed by Korotkov [66 and 67] and his team in St. Petersburg, Russia. Their instrument Crown-TV can be routinely used which opens practical possibilities to study the effects of GDV.
The basic idea of GDV is to create an electromagnetic field using a high-voltage and high-frequency generator. After a threshold voltage is exceeded, the ionization of gas around the studied object takes place and as a side effect, the quanta of light photons are emitted. So the discharge can be fixed optically by a photo, photosensor or TV-camera. Various parameters influence the ionization process [66]: gas properties (gas type, pressure, gas content), voltage parameters (amplitude, frequency, impulse waveform), electrode parameters (configuration, distance, dust and moisture, macro- and microdefects, electromagnetic field configuration), and studied object parameters (common impedance, physical fields, skin galvanic response, etc.). So the Kirlian effect is the result of mechanical, chemical and electromagnetic processes, and field interactions. Gas discharge acts as means of enhancing and visualization of super-weak processes.
Due to the large number of parameters that influence the Kirlian effect, it is very difficult or impossible to control them all, so in the development of discharge there is always an element of vagueness or stochastic. This is one of the reasons why the technique has not yet been widely accepted in practice as results did not have a high reproducibility. All explanations of the Kirlian effect apprehended fluorescence as the emanation of a biological object. Due to the low reproducibility, in academic circles there was a widely spread opinion that all observed phenomena are nothing else but fluctuation of the crown discharge without any connection to the studied object. With modern technology, the reproducibility became sufficient to enable serious scientific studies.
Besides studying non-living objects, such as water and various liquids [66], minerals, the most widely studied are living organisms: plants (leafs, seeds, etc. [66 and 68]), animals [69], and of course humans. For humans, most widely recorded are coronas of fingers [66 and 70] and GDV records of blood excerpts [71]. Principal among these are studies of the psycho-physiological state and energy of a human, diagnosis [72], reactions to some medicines, reactions to various substances, food [70], dental treatment [73], alternative healing treatment, such as acupuncture, ‘bioenergy’, homeopathy, various relaxation and massage techniques [66], GEM therapy, applied kineziology and flower essence treatment [74], leech therapy, etc. and even studying the GDV images after death [67]. There are many studies currently going on all over the world and there is no doubt that the human subtle energy field, as vizualized using the GDV technique, is highly correlated to the human’s psycho-physiological state, and can be used for diagnostics, prognostics, therapy selection, and controlling the effects of the therapy.
4.2.2. Verifying the map of organs
Korotkov’s team has developed a computer program that generates the corona of the whole human body from coronas of all 10 fingertips. The program is based on a map, known from traditional Chinese medicine and described in Mandel’s book [75]. This map defines regions (sectors) of each finger’s corona to be related with a specific organ or organ system in the body. For example, the corona of the left little finger contains sectors that correspond to the coronary vessels, heart, kidney, respiratory system, small intestine, and ileum. Korotkov [66] and his team slightly modified Mandel’s map.
For the orthodox medicine, this map is meaningless, there is no physiological evidence for the connection of fingertips with different organs. Besides, the Kirlian camera is considered to provide only noisy pictures that are not related to human state of health.
In order to verify the map and the hypothesis that Kirlian camera provides useful information, we performed several experiments [76, 77 and 78]. In the following, we briefly describe one such experiment.
We recorded all 10 fingertips of 105 persons that also filled in a questionnaire where they described their health problems. We wanted to distinguish persons, that in the questionnaire had answered that they had no health problem, from persons who had problems with the throat (majority class contained 52.4% of cases). The cases were described by 75 numeric attributes that correspond to areas of sectors of coronas according to the map. We used the C4.5 learning system [58] and the result of 10-fold cross-validation was 14.5% of error. This indicates that coronas in fact contain useful information for diagnosis.
Even more interesting was the structure of trees. For the root of the tree, the algorithm selected most of the times, out of 75 attributes describing sectors of fingers, a sector that corresponds to the throat. There are two such sectors out of 75 sectors (the probability that this could happen by chance for one tree is 2/75=0.027). The other two most important attributes corresponded to jaw and kidney. Jaw is, also by orthodox medicine, related to throat while kidney is by traditional Chinese medicine directly connected with the throat.
This result and several similar studies [76, 77 and 78] indicate that the map of organs makes sense and that it would be beneficial for medicine to study this phenomena and eventually discover the underlying principles.
4.2.3. Overview of other studies
We use Kirlian camera to indirectly record subtle bioelectromagnetic field of living organisms, mostly humans. The obtained images are then described with a set of numerical parameters that serve as an input to statistical and ML algorithms. The subtle energies are not recognized by current orthodox scientific community, and the aim of our studies is to verify "knowledge" of many practitioners in complementary medicine, who claim that living organisms besides physical body contain also non-measurable subtle levels, such as emotional and mental body.
We have performed several studies in which we analyzed the influence of various parameters on plant and human bioelectromagnetic field.
4.2.3.1. Apple skin
We recorded the coronas of apple peels that were cut off from apples in a standard way. We used four sorts of apples of two different ages. We succeeded by the means of ML to extract useful information for distinguishing apples of different sorts and of different ages (the achieved classification accuracy was low but significantly higher than random classification). We were unable to extract any information to distinguish sun/shadow sides of apples [77].
4.2.3.2. Grapes
The aim of the study was to verify whether the Kirlian camera could be used to describe grapevines and if the berry bioelectromagnetic field is influenced by disease. With the Kirlian camera, we recorded coronas of grape berries. We tested this method on eight grapevine cultivars, performing different tests using ML algorithms. The results show that the coronas of grapevine berries contain significant information about the cultivars and their sanitary status [79].
4.2.3.3. Menstrual cycle
For the preliminary study, we recorded coronas of all 10 fingertips of 13 female students in 4 weeks, one recording per week. Each recording was classified into one of four menstrual phases. The results of the analysis indicate that the coronas seem to be correlated with menstrual phases and that sectors of organs make sense. Out of 225 numerical parameters, we automatically extracted 15 most important parameters. Total 14 of those parameters corresponded to sectors of three fingers which by Chinese medicine are directly connected with organs that are by offical medicine affected by/responsible for the menstrual cycle [77].
4.2.3.4. T-shirts
We wanted to evaluate the effect of different T-shirts on the human bioelectromagnetic field: color T-shirts developed by physicist Dr. Tom Chalko from University of Melbourne, ‘healing’ T-shirt developed by Vitalis from Slovenia, and an ordinary black and ordinary white T-shirt.
We measured five groups of people (with a control group). The analysis confirmed that black and white T-shirts have no significant influence on the coronas, while Vitalis and color T-shirts do have positive influence — they improve in time, the coronas of human fingers by means of larger area and lower fragmentation [77].
4.2.3.5. Glass 2000
Vili Poznik from Ceije, Slovenia, uses orgon technology to encode information into a glass which affects in some way the water with which you fill the glass (this is of course nonsense for orthodox science). We recorded coronas of 34 persons before and after drinking the tap water from an ordinary glass and from a Glass 2000, coded by Vili Poznik. The results show that there was a significant improvement of coronas (larger area and lower fragmentation) when drinking water from Glass 2000, while the effect of drinking from ordinary glass was insignificant [79].
4.2.3.6. The art of living
We performed three studies in order to verify the effects of the art of living program (exercises in communication, relaxation, and breathing) on its participants. The results showed significant improvement of coronas (larger area) for participants of a 2 h meeting and of a 6-day seminar compared to control groups which had no significant differences [80].
4.2.3.7. Mobile telephones
We recorded coronas of all 10 fingertips of five groups of persons that were carrying the mobile telephone above their heart for a period of 1 h under different conditions: without any protection, with two different energetic protections (which are nonsense for orthodox science), with placebo (fake) protection and a control group (without mobile telephones). Results indicate that mobile telephones negatively affect the human BEM field, that energetic protections work well while the placebo protection does not work [81].
4.2.3.8. Energetic diagnosis
We recorded coronas of all 10 fingertips of 110 persons for whose the extrasense healer provided the energetic diagnosis. We used machine learning to interpret the GDV coronas in order to verify three hypothesis: (a) the GDV images contain useful information about the patient, (b) the map of organs on coronas of 10 fingers does make sense, and (c) the extrasense healer is able to see by himself (with his natural senses) the energetic disorders in the human body. The results support all three hypotheses [78].
Ref.
71. Voeikov VL. Living blood outside an organism. In: Proceedings of the International Scientific Conference Kirlionics, White Nights 98, 1998 Jun; Federal Technical University SPIFMO, St. Petersburg, Russia.
72. B. Gurvits and K. Korotkov, A new concept of the early diagnosis of cancer. Consciousness Phys. Reality 1 1 (1998), pp. 84–89.
73. Lee SD. The application of Kirlian photography in dentistry. In: Proceedings of the International Scientific Conference Kirlionics, White Nights 98, 1998 Jun; Federal Technical University SPIFMO, St. Petersburg, Russia.
74. Hein M. Bio-synergetix: a new paradigm in subtle energy health care. In: Proceedings of the International Conference on Biology and Cognitive Sciences, 1999 Oct 12–14; Ljubljana, Slovenia, pp. 72–9.
75. Mandel P. Energy emission analysis. Germany: Synthesis Publishing Company, 1986.
76. Kononenko I, Zrimec T, Prihavec B, Bevk M, Stanojevi S. Machine learning and GDV images: diagnosis and therapy verification. In: Proceedings of the International Conference on Biology and Cognitive Sciences, 1999 Oct; Ljubljana, Slovenia, pp. 84–7.
77. Kononenko I, Zrimec T, Sadikov A, Mele K, Milhari T. Machine learning and GDV images: current research and results. In: Proceedings of the International Conference on Biology and Cognitive Science, 1999 Oct; Ljubljana, Slovenia, pp. 80–3.
78. Bevk M, Kononenko I, Zrimec T. Relation between energetic diagnoses and GDV images. In: Proceedings of the New Science of Consciousness: 3rd International Conference on Cognitive Science, 2000 Oct; Ljubljana, Slovenia, pp. 54–7.
79. Kononenko I, Zrimec T, Sadikov A, Skoaj D. GDV images: current research and results. In: Proceedings of the New Science of Consciousness: 3rd International Conference on Cognitive Science, 2000 Oct; Ljubljana, Slovenia, pp. 65–8.
80. A. Trampu, I. Kononenko and V. Rus, Experiental and biophysical effects of the art of living programme. Int. J. Psychol. 35 3/4 (2000), p. 12.
81. Kononenko I, Bosni Z, gajnar B. The influence of mobile telephones on human bioelectromagnetic field. In: Proceedings of the New Science of Consciousness: 3rd International Conference on Cognitive Science, 2000 Oct; Ljubljana, Slovenia, pp. 69–72.
