Fuzzy decision support systems to diagnose musculoskeletal disorders: A systematic literature review

Model of the Performance Based on Artificial Intelligence-Fuzzy Logic Description of Physical Activity

The aim of the study was to build a fuzzy model of lower limb peak torque in an isokinetic mode. The study involved 93 male participants (28 male deaf soccer players, 19 hearing soccer players and 46 deaf untraining male). A fuzzy computational model of different levels of physical activity with a focus on the lower limbs was constructed. The proposed fuzzy model assessing lower limb peak torque in an isokinetic mode demonstrated its effectiveness. The novelty of our research lies in the use of hierarchical fuzzy logic to extract computational rules from data provided explicitly and then to determine the corresponding physiological and pathological mechanisms. The contribution of our research lies in complementing the methods for describing physiology, pathology and rehabilitation with fuzzy parameters, including the so-called dynamic norm embedded in the model.

Intelligent System for Detecting Deterioration of Life Satisfaction as Tool for Remote Mental-Health Monitoring

The research described in this article is a continuation of work on a computational model of quality of life (QoL) satisfaction. In the proposed approach, overall life satisfaction is aggregated to personal life satisfaction (PLUS). The model described in the article is based on well-known and commonly used clinimetric scales (e.g., in psychiatry, psychology and physiotherapy). The simultaneous use of multiple scales, and the complexity of describing the quality of life with them, require complex fuzzy computational solutions. The aim of the study is twofold: (1) To develop a fuzzy model that allows for the detection of changes in life satisfaction scores (data on the influence of the COVID-19 pandemic and the war in the neighboring country were used). (2) To develop more detailed guidelines than the existing ones for further similar research on more advanced intelligent systems with computational models which allow for sensing, detecting and evaluating the psychical state. We are concerned with developing practical solutions with higher scientific and clinical utility for both small datasets and big data to use in remote patient monitoring. Two exemplary groups of specialists at risk of occupational burnout were assessed three times at different intervals in terms of life satisfaction. The aforementioned assessment was made on Polish citizens because the specific data could be gathered: before and during the pandemic and during the war in Ukraine (a neighboring country). That has a higher potential for presenting a better analysis and reflection on the practical application of the model. A research group (physiotherapists, n = 20) and a reference group (IT professionals, n = 20) participated in the study. Four clinimetric scales were used for assessment: the Perceived Stress Scale (PSS10), the Maslach Burnout Scale (MBI), the Satisfaction with Life Scale (SWLS), and the Nordic Musculoskeletal Questionnaire (NMQ). The assessment was complemented by statistical analyses and fuzzy models based on a hierarchical fuzzy system. Although several models for understanding changes in life satisfaction scores have been previously investigated, the novelty of this study lies in the use of data from three consecutive time points for the same individuals and the way they are analyzed, based on fuzzy logic. In addition, the new hierarchical structure of the model used in the study provides flexibility and transparency in the process of remotely monitoring changes in people's mental well-being and a quick response to observed changes. The aforementioned computational approach was used for the first time.

A Logistic Regression Model for Biomechanical Risk Classification in Lifting Tasks

Lifting is one of the most potentially harmful activities for work-related musculoskeletal disorders (WMSDs), due to exposure to biomechanical risk. Risk assessment for work activities that involve lifting loads can be performed through the NIOSH (National Institute of Occupational Safety and Health) method, and specifically the Revised NIOSH Lifting Equation (RNLE). Aim of this work is to explore the feasibility of a logistic regression model fed with time and frequency domains features extracted from signals acquired through one inertial measurement unit (IMU) to classify risk classes associated with lifting activities according to the RNLE. Furthermore, an attempt was made to evaluate which are the most discriminating features relating to the risk classes, and to understand which inertial signals and which axis were the most representative. In a simplified scenario, where only two RNLE variables were altered during lifting tasks performed by 14 healthy adults, inertial signals (linear acceleration and angular velocity) acquired using one IMU placed on the subject's sternum during repeated rhythmic lifting tasks were automatically segmented to extract several features in the time and frequency domains. The logistic regression model fed with significant features showed good results to discriminate "risk" and "no risk" NIOSH classes with an accuracy, sensitivity and specificity equal to 82.8%, 84.8% and 80.9%, respectively. This preliminary work indicated that a logistic regression model-fed with specific inertial features extracted by signals acquired using a single IMU sensor placed on the sternum-is able to discriminate risk classes according to the RNLE in a simplified context, and therefore could be a valid tool to assess the biomechanical risk in an automatic way also in more complex conditions (e.g., real working scenarios).

Effect of COVID-19 on Selected Characteristics of Life Satisfaction Reflected in a Fuzzy Model

The general goal of the research in this article is to devise an algorithm for assessing overall life satisfaction—a term often referred to as Quality of Life (QoL). It is aggregated to its own proposition, called personal life usual satisfaction (PLUS). An important assumption here is that the model is based on already known and commonly used solutions, such as medical (psychological and physiotherapeutic) questionnaires. Thanks to this, the developed solution allows us to obtain a synergy effect from the existing knowledge, without the need to design new, complicated procedures. Fuzzy multivariate characterization of life satisfaction presents a challenge for a complete analysis of the phenomenon. The complexity of description using multiple scales, including linguistic, requires additional computational solutions, as presented in this paper. The detailed aim of this study is twofold: (1) to develop a fuzzy model reflecting changes in life satisfaction test scores as influenced by the corona virus disease 2019 (COVID-19) pandemic, and (2) to develop guidelines for further research on more advanced models that are clinically useful. Two groups affected by professional burnout to different degrees were analyzed toward life satisfaction twice (pre- and during pandemy): a study group (physiotherapists, n=25) and a reference group (computer scientists, n=25). The Perceived Stress Score (PSS10), Maslach Burnout Inventory (MBI), Satisfaction with Life Scale (SWLS), and Nordic Musculoskeletal Questionnaire (NMQ) were used. The resultant model is based on a hierarchical fuzzy system. The novelty of the proposed approach lies in the combination of the use of data from validated clinimetric tests with the collection of data from characteristic time points and the way in which they are analyzed using fuzzy logic through transparent and scalable hierarchical models. To date, this approach is unique and has no equivalent in the literature. Thanks to the hierarchical structure, the evaluation process can be defined as a modular construction, which increases transparency and makes the whole procedure more flexible.

The role of machine learning in the primary prevention of work-related musculoskeletal disorders: A scoping review

To determine the applications of machine learning (ML) techniques used for the primary prevention of work-related musculoskeletal disorders (WMSDs), a scoping review was conducted using seven literature databases. Of the 4,639 initial results, 130 primary research studies were deemed relevant for inclusion. Studies were reviewed and classified as a contribution to one of six steps within the primary WMSD prevention research framework by van der Beek et al. (2017). ML techniques provided the greatest contributions to the development of interventions (48 studies), followed by risk factor identification (33 studies), underlying mechanisms (29 studies), incidence of WMSDs (14 studies), evaluation of interventions (6 studies), and implementation of effective interventions (0 studies). Nearly a quarter (23.8%) of all included studies were published in 2020. These findings provide insight into the breadth of ML techniques used for primary WMSD prevention and can help identify areas for future research and development.

Developing a clinical decision support system based on the fuzzy logic and decision tree to predict colorectal cancer

Background: Colorectal Cancer (CRC) is the most prevalent digestive system- related cancer and has become one of the deadliest diseases worldwide. Given the poor prognosis of CRC, it is of great importance to make a more accurate prediction of this disease. Early CRC detection using computational technologies can significantly improve the overall survival possibility of patients. Hence this study was aimed to develop a fuzzy logic-based clinical decision support system (FL-based CDSS) for the detection of CRC patients. Methods: This study was conducted in 2020 using the data related to CRC and non-CRC patients, which included the 1162 cases in the Masoud internal clinic, Tehran, Iran. The chi-square method was used to determine the most important risk factors in predicting CRC. Furthermore, the C4.5 decision tree was used to extract the rules. Finally, the FL-based CDSS was designed in a MATLAB environment and its performance was evaluated by a confusion matrix. Results: Eleven features were selected as the most important factors. After fuzzification of the qualitative variables and evaluation of the decision support system (DSS) using the confusion matrix, the accuracy, specificity, and sensitivity of the system was yielded 0.96, 0.97, and 0.96, respectively. Conclusion: We concluded that developing the CDSS in this field can provide an earlier diagnosis of CRC, leading to a timely treatment, which could decrease the CRC mortality rate in the community.

An intuitionistic intervals based hesitant FQA-TOPSIS for vitiligo grading

Vitiligo is a problem due to the destruction of melanocytes which is present in 1% of people all over the world. The origin of this disease is unknown and difficult to cure. Absence of melanin in the body causes lesions which will ooze out all over the body. Phototherapy and surgical therapy are the two types of treatment available in the existing world. The Cytotoxic CD8 + T lymphocytes act as a major factor for the abovesaid disease. The main objective of this work is to treat patients with different methods according to the severity of vitiligo, which can be identified with the help of out ranking based on hesitant fuzzy relation. Multi-criteria and multi-objective hesitant fuzzy are applied in this work to find out the ranking through which the severity of the disease is detected. This method helps in identifying the vitiligo lesions, which can be treated effectively in a short period of time. During the application of vitiligo treatment, FQA-TOPSIS (Fuzzy Quantified Attribute-TOPSIS) hesitant fuzzy relation methodology is deployed with three decision maker’s support using linguistic and intuitionistic values. The decision maker’s fuzzy values will be normalized and aggregated in this work with improved methodologies. The two objectives are deployed with their own fuzzy values and are implemented in the decision maker’s values. In the article fuzzy weightage has been calculated in two ways. One is every linguistic like low, medium, high and very high has got its significant intuitionistic values that all will be available with the scale of 1 to 10. The same has given as triplets. In our research work the above said has applied with the objective based weightage. So the accuracy has been increased through the work. The outcome of this methodology is to find out the coefficient closeness of the alternatives and to out rank the decision alternatives. The difference between the Final +ve Ideal Solution (FPIS) and Final -ve Ideal Solution (FNIS) is determined and FQA-TOPSIS Hesitant Fuzzy is ranked in the result.