Application of the Structure Function in the Evaluation of the Human Factor in Healthcare

“…The development of these studies has resulted in the extending of the conception of the subject of investigation in this area. At the present time, the methods and approaches of reliability engineering are used for analysis and evaluation of different system types (objects), which include human factors [1], maintainability [2], environmental factors [3], and social impact [4]. This has led to the application of reliability engineering in non-typical areas for risk and reliability analysis by these methods.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, two principal directions of studies in healthcare safety has been considered [1]. One of them is named "technical".…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, the Fuzzy Decision Tree is used for the classification of initial data to construct the structure function. The advantage of this classifier is the possibility of visualisation of the process of decision making, which is very important for healthcare application [1,39].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, the quantification of these factors is performed based on an importance analysis approach, which in reliability engineering is used to evaluate the influence of the system state change depending on the change of its component state [40,41]. Many algorithms in importance analysis are developed for the mathematical representation of the investigated system in the form of the structure function [1,32,40,41]. These algorithms can be used for quantification of the factors influencing the COVID-19 patient's condition without modification, which simplifies the evaluation.…”

Section: Introductionmentioning

confidence: 99%

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Reliability Evaluation of the Factors That Influenced COVID-19 Patients’ Condition

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Health and safety is a problem that is intensively discussed nowadays. The failures in healthcare are called medical errors: if the patient’s condition worsens or he/she contracts an illness, then the actions that led to this are interpreted as medical errors. Medical errors can be the result of new procedures, extremes of age, complex or urgent care, improper documentation, illegible hand-writing, or patient actions. One of the ways to reduce medical error is an evaluation of its possibility, and then using the result of this evaluation to improve the medical organization units and processes in patient diagnosis, treatment, and care. This evaluation is possible based on methods of reliability engineering. The reliability engineering methods allow evaluating of different systems’ reliability and the influence of external and internal factors on system reliability. These methods’ application needs the system to be investigated or objective interpretation in terms of reliability engineering. Therefore, such a system in healthcare, for the diagnosis of disease, a patient’s treatment, the influence of different factors on a patient’s condition, and others, should be presented according to the rules and demands of reliability engineering. The first step is development of the mathematical representation of the investigated system or object according to the demands of the reliability analysis. One of the often-used mathematical representations in the reliability analysis of a system is the structure function. However, this mathematical representation needs completely specified initial data. The initial data from the healthcare domain for medical error analysis is uncertain and incompletely specified. Therefore, the development of this mathematical representation needs special methods. In this paper, a new method for the mathematical representation of system development based on uncertain and incompletely specified data is proposed. The system evaluation based on the structure function allows computing of many reliability indices and measures used in reliability engineering. The approbation of this method is considered based on an example of COVID-19 patients.

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An intelligent human‐centric systems to diagnose breast cancer using machine learning and optimized feature selection techniques

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All over the world, breast cancer (BC) is the leading cause of cancer mortality among women. Computer‐aided methods can assist in early diagnosis. The proposed approach used SMOTE filter with Ch2 test techniques for class balance and feature section using eight different ML models Gaussian Naive Bayes (GNB), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM) with Linear and Radial Basis Function (RBF), Logistic Regression (LR), K‐nearest neighbor (KNN) and eXtreme Gradient Boosting (XGBoost). A Ch2 test determines the top five features—glucose, HOMA, resistin, BMI, and insulin. Metrics such as accuracy, precision, recall, and F1‐Score are used to compare the performance of models. More than 99% accuracy was achieved by the proposed XGBoost model. Compared to the other breast cancer prediction models, the proposed model had an average accuracy improvement of 9.30%. As a result of our proposed model, breast cancer diagnosis will be more efficient based on risk factors. The proposed prediction model can also predict various breast cancer features. In addition to improving diagnostic decision‐support systems, the proposed model should be able to predict breast cancer disease accurately.

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Application of the Structure Function in the Evaluation of the Human Factor in Healthcare

Cited by 22 publications

References 71 publications

Building Decision Support Systems based on Fuzzy Data

Building Decision Support Systems based on Fuzzy Data

Reliability Evaluation of the Factors That Influenced COVID-19 Patients’ Condition

An intelligent human‐centric systems to diagnose breast cancer using machine learning and optimized feature selection techniques

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