A Method for Automatic Medical Diagnosis

  • Igor Grabec Amanova Ltd, Technology park of Ljubljana
  • Eva Švegl Faculty of Medicine, University of Ljubljana, SI-1000 Ljubljana, Slovenia
  • Mihael Sok University Medical Center, SI-1000 Ljubljana, Slovenia
DOI: https://doi.org/10.19139/soic.v7i1.414
Keywords: Automatic Statistical Diagnosis, Non-parametric Inference, Disease Likelihood

Abstract

This research paper presents a new method for the automatic diagnosis of diseases using a personal computer. Forming a basis for the characterization of diseases, a wide set of symptoms is introduced, and a particular disease is characterized by a set of statistical weights assigned to those symptoms. Information about the patient’s state is provided by a graphic interface in which the user confirms symptom indicators. Agreement between these symptoms and classified symptoms of a particular disease is then estimated by the sum of corresponding weights, where the disease corresponding to the maximal agreement is proposed as the result of the diagnosis. A disease likelihood estimator is calculated and presented to assess the reliability of the diagnosis. With regard to the automatic assessment of the diagnosis the corresponding algorithm and the properties of the computer program are included. Finally, the effectiveness of this method of medical diagnosis is demonstrated through four typical examples involving differently expressed symptoms. The diagnostic system resembles semantically driven sensory-neural network.

Author Biographies

Igor Grabec, Amanova Ltd, Technology park of Ljubljana
Academitian, Emeritus Professor, University of ljubljana
Eva Švegl, Faculty of Medicine, University of Ljubljana, SI-1000 Ljubljana, Slovenia
Student
Mihael Sok, University Medical Center, SI-1000 Ljubljana, Slovenia
Univ. Professor

References

I. Kononenko, Machine learning for medical diagnosis: history, state of the art and perspective, Artificial Intelligence in Medicine, vol. 23, no. 1, 89-109, 2001.

M. Sok, E. Švegl, and I. Grabec Statistical diagnosis of diseases, Proc. Int. Conf. Applied Statistics, Ribno (Bled) Slovenia, Sept. 18-21, 2016, Abstracts and Program, p30.

Call for project proposals, https://en.wikipedia.org/wiki/Tricorder X Prize, 2013.

S. El-Sappagh, M. Elmogy, A.M. Riad, A fuzzy-ontology-oriented case-

based reasoning framework for semantic diabetes diagnosis, Artificial Intelligence in Medicine, vol. 65, no. 3, 179-208, 2015.

K. B. Wagholikar, V. Sundararajan, and A. W. Deshpande, Modeling paradigms for medical diagnostic decision support: a survey and future directions, Journal of Medical Systems, vol. 36, no. 5, 3029-3049, 2012.

Merriam-Webster’s Dictionary,

http://learnersdictionary.com/definition/diagnosis.

Wikipedia, https://en.wikipedia.org/wiki/Medical diagnosis.

E. P. Balogh, B. T. Miller, and J. R. Ball, Eds; Committee on Diagnostic Error in Health Care, Board on Health Care Services; Institute ofMedicine; The National Academy of Sciences, Engineering and Medicine, Improving Diagnosis in Health Care, The National Academic Press, Washington DC,

X. H. Zhou, N. A. Obuchowski, and D. K. McClish, Statistical Methods in Diagnostic Medicine, Wiley series in probability and statistics, 2nd Edition, 2011.

I. Grabec, and W. Sachse, Synergetics of Measurement, Prediction and Control, Springer, Berlin, 1997.

A. Sadikov, V. Groznik, J. Žabkar, M. Možina, D. Georgiev, Z. Pirtošek, I. Bratko, ParkinsonCheck smart phone application, In: T. Schaub, G.Friedrich, and B. O’Sullivan, Eds., Proc. of ECAI Frontiers in Artificial Intelligence and Applications, p. 263, IOS Press, Amsterdam, 2014.

T. Shaikhina, N. A. Khovanova, Handling limited datasets with neural networks in medical applications: A small-data approach, Artificial Intelligence in Medicine, vol. 75, 51-63, January, 2017.

C.-L. Chi, W. N. Street, D. A. Katz, A decision support system for cost-effective diagnosis, Artificial Intelligence in Medicine, vol. 50, no. 3, 149-161, 2010.

https://www.google.si/?gws_ rd=ssl#q=diagnosis+of+diseases+by+symptoms,

http://www.healthline.com/symptom-checker.

M. Sok, E. Švegl, and I. Grabec, Sensory-neural Network for Medical Diagnosis, Evolving and Adaptive Intelligent Systems EAIS 2017, May 31 June 2, 2017, Ljubljana, Slovenia, http://msc.fe.uni-lj.si/eais2017, Publisher: https://www.ieee.org

Published
2019-01-07
How to Cite
Grabec, I., Švegl, E., & Sok, M. (2019). A Method for Automatic Medical Diagnosis. Statistics, Optimization & Information Computing, 7(1), 26-39. https://doi.org/10.19139/soic.v7i1.414
Issue
Vol 7 No 1 (2019)
Section
Research Articles
Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).