ISSN 2071-8594

Russian academy of sciences


Gennady Osipov

N. A. Ignatev, E. N. Zguralskaya, M. V. Markovtseva Search for Hidden Patterns Affecting Overall Patient Survival with Data Mining


The reasons affecting the survival time of patients with chronic lymphocytic leukemia, taking into account gender, are studied. The set of patients is divided into two disjoint subsets (classes) by the indicator of actual survival, the value of which is less than the predicted value of overall survival, is determined. To detect hidden patterns in the analysis, nonlinear data transformations based on the calculation of the values of the class membership function for each attribute are used. The threshold values between the classes on the numerical axis are determined, both by individual attributes and by generalized assessments of objects on defined sets of attributes. The threshold values are used to record logical patterns in the form of half-planes and display gender differences for predicting the survival of patients.


data mining, membership function, overall survival rate, generalized object assessment.

DOI 10.14357/20718594200307

PP. 73-80.


1. Savchenko, V.G. 2018. Algoritmy diagnostiki i protokoly lechenija zabolevanij sistemy krovi [Diagnostic algorithms and protocols for the treatment of blood system diseases] Moscow: Practice. 307 p.
2. Rai, K.R., A. Sawitsky, and E.P. Cronkite. 1975. Clinical staging of chronic lymphocytic leukemia. Blood 46:219-234.
3. Binet, J. L., A. Auquier, and G.L. Dighiero. 1981. A new prognostic classification of chronic lymphocytic leukemia derived from a multivariate survival analysis. Cancer 48:198–206.
4. Shumakov, V.I., V.N. Novosel'cev, M.P. Saharov, and E.Sh. Shtengold. 1971. Modelirovanie fiziologicheskih sistem organizma [Modeling the physiological systems of the body]. Moscow:Medicine. 352 p.
5. Zguralskaya, E.N. 2018. Ustojchivost' razbienija dannyh na intervaly v zadachah raspoznavanija i poisk skrytyh zakonomernostej [The stability of dividing data into intervals in recognition problems and the search for hidden patterns]. Izvestija Samarskogo nauchnogo centra Rossijskoj akademii nauk [Bulletin of the Samara Scientific Center of the Russian Academy of Sciences] 20(4):451–455.
6. Ignatev, N.A. 2011. Vychisleniye obobshchennykh pokazateley i intellektualnyy analiz dannykh [Calculation of generalized indicators and data mining]. Avtomatika i telemekhanika [Automation and Remote Control] 5:183–190.
7. Madrakhimov, Sh. 2018. Calculation of the Generalized Estimations in Sets of Features and their Interpretation. International Journal of Software Engineering and Its Applications 12(3):29–38.
8. Piatetsky-Shapiro, G. 2007. Data mining and knowledge discovery 1996 to 2005: overcoming the hype and moving from “university” to “business” and “analytics”. Data Mining and Knowledge Discovery 15:99–105.