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

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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).

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Abbreviations

ALL:

Acute lymphocytic leukemia

AML:

Acute myelocytic leukemia

ADR:

Adverse drug reaction

ANN:

Artificial neural network

ASD:

Autism spectrum disorder

BHMM:

Bayesian hidden Markov model

BN:

Bayesian network

BF:

Bootstrap forest

CBFCM:

Case-based fuzzy cognitive maps

CSONN:

Cat swarm optimization neural network

CNS:

Central nervous system

CXR:

Chest X-ray

CLL:

Chronic lymphocytic leukemia

CML:

Chronic myelogenous leukemia

CBC:

Complete blood count

CT:

Computed tomography

DEA:

Data envelopment analysis

DSS:

Decision support system

DNA:

Deoxyribonucleic acid

EEG:

Electroencephalogram

ESR:

Erythrocyte sedimentation rate

EBP:

Evidence-based practice

FCM:

Fuzzy cognitive map

HSCT:

Hematopoietic stem cell transplant

Hb:

Hemoglobin

HIV:

Human immunodeficiency virus

LDH:

Lactate dehydrogenase

LSTM:

Long short-term memory network

MRI:

Magnetic resonance imaging

MCH:

Mean corpuscular hemoglobin

MCV:

Mean corpuscular volume

MDSS:

Medical decision support system

NHL:

Nonlinear Hebbian learning

PSO:

Particle swarm optimization

Plt:

Platelet

PC:

Prototypical constraint-based

RNN:

Recurrent neural network

RBC:

Red blood cell count

SVM:

Support vector machine

TAN:

Tree augmented naïve

UTI:

Urinary tract infection

WBC:

White blood cell

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Authors and Affiliations

  1. Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran

    Mustafa Jahangoshai Rezaee, Maryam Sadatpour, Nazli Ghanbari-ghoushchi & Ehsan Fathi

  2. Department of Internal Medicine, Urmia University of Medical Sciences, Urmia, Iran

    Azra Alizadeh

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  1. Mustafa Jahangoshai Rezaee
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  2. Maryam Sadatpour
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  3. Nazli Ghanbari-ghoushchi
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  4. Ehsan Fathi
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  5. Azra Alizadeh
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Correspondence to Mustafa Jahangoshai Rezaee.

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Jahangoshai Rezaee, M., Sadatpour, M., Ghanbari-ghoushchi, N. et al. Analysis and decision based on specialist self-assessment for prognosis factors of acute leukemia integrating data-driven Bayesian network and fuzzy cognitive map. Med Biol Eng Comput 58, 2845–2861 (2020). https://doi.org/10.1007/s11517-020-02267-w

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  • DOI: https://doi.org/10.1007/s11517-020-02267-w

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