Analysis and decision based on specialist self-assessment for prognosis factors of acute leukemia integrating data-driven Bayesian network and fuzzy cognitive map.

Abstract:

:The purpose of the present study is to analyze the prognostic factors of acute leukemia and to construct a decision model based on a causal relationship between the factors of this disease to assist medical specialists. In medical decisions, to reach effective, quick, and reliable results, there is a need for a simple decision-making model based on a specialist's self-assessment. It may help the medical team before final diagnosis by costly and time-consuming procedures such as bone marrow sampling and pathological test as well as provide an appropriate prognosis and diagnosis tool. Because of the complex and not the well-defined structure of medical data, the use of intelligent methods must be considered. For this purpose, first, a data-driven Bayesian network (BN) and Greedy algorithm are employed to determine causal relationships and probability between nodes using the real set of data. Then, these causal relationships will form based on the fuzzy cognitive map (FCM). Finally, according to scenarios defined, the results are analyzed. These analyses are also repeated for each type of acute leukemia including acute lymphocytic leukemia (ALL) and acute myelocytic leukemia (AML). Graphical abstract.

journal_name

Med Biol Eng Comput

authors

Jahangoshai Rezaee M,Sadatpour M,Ghanbari-Ghoushchi N,Fathi E,Alizadeh A

doi

10.1007/s11517-020-02267-w

subject

Has Abstract

pub_date

2020-11-01 00:00:00

pages

2845-2861

issue

11

eissn

0140-0118

issn

1741-0444

pii

10.1007/s11517-020-02267-w

journal_volume

58

pub_type

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