A fuzzy information-based approach for breast cancer risk factors assessment

Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study

Fuzzy Cognitive Maps (FCMs) have become an invaluable tool for healthcare providers because they can capture intricate associations among variables and generate precise predictions. FCMs have demonstrated their utility in diverse medical applications, from disease diagnosis to treatment planning and prognosis prediction. Their ability to model complex relationships between symptoms, biomarkers, risk factors, and treatments has enabled healthcare providers to make informed decisions, leading to better patient outcomes. This review article provides a thorough synopsis of using FCMs within the medical domain. A systematic examination of pertinent literature spanning the last two decades forms the basis of this overview, specifically delineating the diverse applications of FCMs in medical realms, including decision-making, diagnosis, prognosis, treatment optimisation, risk assessment, and pharmacovigilance. The limitations inherent in FCMs are also scrutinised, and avenues for potential future research and application are explored.

Length of hospital stay prediction with an integrated approach of statistical-based fuzzy cognitive maps and artificial neural networks

Chronic obstructive pulmonary disease (COPD) is a global burden, which is estimated to be the third leading cause of death worldwide by 2030. The economic burden of COPD grows continuously because it is not a curable disease. These conditions make COPD an important research field of artificial intelligence (AI) techniques in medicine. In this study, an integrated approach of the statistical-based fuzzy cognitive maps (SBFCM) and artificial neural networks (ANN) is proposed for predicting length of hospital stay of patients with COPD, who admitted to the hospital with an acute exacerbation. The SBFCM method is developed to determine the input variables of the ANN model. The SBFCM conducts statistical analysis to prepare preliminary information for the experts and then collects expert opinions accordingly, to define a conceptual map of the system. The integration of SBFCM and ANN methods provides both statistical data and expert opinion in the prediction model. In the numerical application, the proposed approach outperformed the conventional approach and other machine learning algorithms with 79.95% accuracy, revealing the power of expert opinion involvement in medical decisions. A medical decision support framework is constructed for better prediction of length of hospital stay and more effective hospital management.

Fuzzy cognitive maps in systems risk analysis: a comprehensive review

Fuzzy cognitive maps (FCMs) have been widely applied to analyze complex, causal-based systems in terms of modeling, decision making, analysis, prediction, classification, etc. This study reviews the applications and trends of FCMs in the field of systems risk analysis to the end of August 2020. To this end, the concepts of failure, accident, incident, hazard, risk, error, and fault are focused in the context of the conventional risks of the systems. After reviewing risk-based articles, a bibliographic study of the reviewed articles was carried out. The survey indicated that the main applications of FCMs in the systems risk field were in management sciences, engineering sciences and industrial applications, and medical and biological sciences. A general trend for potential FCMs’ applications in the systems risk field is provided by discussing the results obtained from different parts of the survey study.

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

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.

A novel approach recommendation for hazard analysis

Hazard evaluation generally defines the ranking of hazards in the work environment and ignores the interaction of hazards. This article aims to overcome this drawback using a fuzzy cognitive map (FCM) approach to analyze the interaction of hazards related to sheathing tasks in a construction firm. The FCM approach can be insufficient for hazard evaluation over the long term due to certain budget and time restrictions allocated by the firm. When the firm allocates more time and budget for a process, the firm must limit the allocated time and budget for the other processes. This article aims to overcome this problem so linear programming is used to optimize the goal and resources of the firm. The article contributes to hazard evaluation of sheathing tasks in a construction firm considering the interaction of the hazards and the capacity of the firm.

Persistent Increased Frequency of Genomic Instability in Women Diagnosed with Breast Cancer: Before, during, and after Treatments

This study aimed to evaluate DNA damage in patients with breast cancer before treatment (background) and after chemotherapy (QT) and radiotherapy (RT) treatment using the Comet assay in peripheral blood and the micronucleus test in buccal cells. We also evaluated repair of DNA damage after the end of RT, as well as the response of patient's cells before treatment with an oxidizing agent (H2O2; challenge assay). Fifty women with a mammographic diagnosis negative for cancer (control group) and 100 women with a diagnosis of breast cancer (followed up during the treatment) were involved in this study. The significant DNA damage was observed by increasing in the index and frequency of damage along with the increasing of the frequency of micronuclei in peripheral blood and cells of the buccal mucosa, respectively. Despite the variability of the responses of breast cancer patients, the individuals presented lesions on the DNA, detected by the Comet assay and micronucleus Test, from the diagnosis until the end of the oncological treatment and were more susceptible to oxidative stress. We can conclude that the damages were due to clastogenic and/or aneugenic effects related to the neoplasia itself and that they increased, especially after RT.

Correlations between Risk Factors for Breast Cancer and Genetic Instability in Cancer Patients-A Clinical Perspective Study

Molecular epidemiological studies have identified several risk factors linking to the genes and external factors in the pathogenesis of breast cancer. In this sense, genetic instability caused by DNA damage and DNA repair inefficiencies are important molecular events for the diagnosis and prognosis of therapies. Therefore, the objective of this study was to analyze correlation between sociocultural, occupational, and lifestyle risk factors with levels of genetic instability in non-neoplastic cells of breast cancer patients. Total 150 individuals were included in the study that included 50 breast cancer patients submitted to chemotherapy (QT), 50 breast cancer patients submitted to radiotherapy (RT), and 50 healthy women without any cancer. Cytogenetic biomarkers for apoptosis and DNA damage were evaluated in samples of buccal epithelial and peripheral blood cells through micronuclei and comet assay tests. Elder age patients (61-80 years) had higher levels of apoptosis (catriolysis by karyolysis) and DNA damage at the diagnosis (baseline damage) with increased cell damage during QT and especially during RT. We also reported the increased frequencies of cytogenetic biomarkers in patients who were exposed to ionizing radiation as well as for alcoholism and smoking. QT and RT induced high levels of fragmentation (karyorrhexis) and nuclear dissolution (karyolysis) and DNA damage. Correlations were observed between age and karyorrhexis at diagnosis; smoking and karyolysis during RT; and radiation and karyolysis during QT. These correlations indicate that risk factors may also influence the genetic instability in non-neoplastic cells caused to the patients during cancer therapies.

A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and applications

BACKGROUND AND OBJECTIVE

A high percentage of medical errors, committed because of physician's lack of experience, huge volume of data to be analyzed, and inaccessibility to medical records of previous patients, can be reduced using computer-aided techniques. Therefore, designing more efficient medical decision-support systems (MDSSs) to assist physicians in decision-making is crucially important. Through combining the properties of fuzzy logic and neural networks, fuzzy cognitive maps (FCMs) are among the latest, most efficient, and strongest artificial intelligence techniques for modeling complex systems. This review study is conducted to identify different FCM structures used in MDSS designs. The best structure for each medical application can be introduced by studying the properties of FCM structures.

METHODS

This paper surveys the most important decision- making methods and applications of FCMs in the medical field in recent years. To investigate the efficiency and capability of different FCM models in designing MDSSs, medical applications are categorized into four key areas: decision-making, diagnosis, prediction, and classification. Also, various diagnosis and decision support problems addressed by FCMs in recent years are reviewed with the goal of introducing different types of FCMs and determining their contribution to the improvements made in the fields of medical diagnosis and treatment.

RESULTS

In this survey, a general trend for future studies in this field is provided by analyzing various FCM structures used for medical purposes, and the results from each category.

CONCLUSIONS

Due to the unique specifications of FCMs in integrating human knowledge and experience with computer-aided techniques, they are among practical instruments for MDSS design. In the not too distant future, they will have a significant role in medical sciences.