Integration of fuzzy-weighted zero-inconsistency and fuzzy decision by opinion score methods under a q-rung orthopair environment: A distribution case study of COVID-19 vaccine doses.

Owing to the limitations of Pythagorean fuzzy and intuitionistic fuzzy sets, scientists have developed a distinct and successive fuzzy set called the q-rung orthopair fuzzy set (q-ROFS), which eliminates restrictions encountered by decision-makers in multicriteria decision making (MCDM) methods and facilitates the representation of complex uncertain information in real-world circumstances. Given its advantages and flexibility, this study has extended two considerable MCDM methods the fuzzy-weighted zero-inconsistency (FWZIC) method and fuzzy decision by opinion score method (FDOSM) under the fuzzy environment of q-ROFS. The extensions were called q-rung orthopair fuzzy-weighted zero-inconsistency (q-ROFWZIC) method and q-rung orthopair fuzzy decision by opinion score method (q-ROFDOSM). The methodology formulated had two phases. The first phase 'development' presented the sequential steps of each method thoroughly.The q-ROFWZIC method was formulated and used in determining the weights of evaluation criteria and then integrated into the q-ROFDOSM for the prioritisation of alternatives on the basis of the weighted criteria. In the second phase, a case study regarding the MCDM problem of coronavirus disease 2019 (COVID-19) vaccine distribution was performed. The purpose was to provide fair allocation of COVID-19 vaccine doses. A decision matrix based on an intersection of 'recipients list' and 'COVID-19 distribution criteria' was adopted. The proposed methods were evaluated according to systematic ranking assessment and sensitivity analysis, which revealed that the ranking was subject to a systematic ranking that is supported by high correlation results over different scenarios with variations in the weights of criteria.

Rise of multiattribute decision-making in combating COVID-19: A systematic review of the state-of-the-art literature.

Considering the coronavirus disease 2019 (COVID-19) pandemic, the government and health sectors are incapable of making fast and reliable decisions, particularly given the various effects of decisions on different contexts or countries across multiple sectors. Therefore, leaders often seek decision support approaches to assist them in such scenarios. The most common decision support approach used in this regard is multiattribute decision-making (MADM). MADM can assist in enforcing the most ideal decision in the best way possible when fed with the appropriate evaluation criteria and aspects. MADM also has been of great aid to practitioners during the COVID-19 pandemic. Moreover, MADM shows resilience in mitigating consequences in health sectors and other fields. Therefore, this study aims to analyse the rise of MADM techniques in combating COVID-19 by presenting a systematic literature review of the state-of-the-art COVID-19 applications. Articles on related topics were searched in four major databases, namely, Web of Science, IEEE Xplore, ScienceDirect, and Scopus, from the beginning of the pandemic in 2019 to April 2021. Articles were selected on the basis of the inclusion and exclusion criteria for the identified systematic review protocol, and a total of 51 articles were obtained after screening and filtering. All these articles were formed into a coherent taxonomy to describe the corresponding current standpoints in the literature. This taxonomy was drawn on the basis of four major categories, namely, medical (n = 30), social (n = 4), economic (n = 13) and technological (n = 4). Deep analysis for each category was performed in terms of several aspects, including issues and challenges encountered, contributions, data set, evaluation criteria, MADM techniques, evaluation and validation and bibliography analysis. This study emphasised the current standpoint and opportunities for MADM in the midst of the COVID-19 pandemic and promoted additional efforts towards understanding and providing new potential future directions to fulfil the needs of this study field.

A hybrid mathematical modelling approach for energy generation from hazardous waste during the COVID-19 pandemic.

The COVID-19 virus in a short time has caused a terrible crisis that has been spread around the world. This crisis has affected human life in several dimensions, one of which is a sharp increase in urban waste. This increase in waste volume during the pandemic, in addition to the intense increase in costs associated with the risks of virus contagion through infectious waste. In this study, a hybrid mathematical modelling approach including a Bi-level programming model for infectious waste management has been proposed. At the higher level of the model, government decisions regarding the total costs related to infectious waste must be minimized. At this level, the collected infectious waste is converted into energy, the revenue of which is returned to the system. The lower level relates to the risks of virus contagion through infectious waste, which can be catastrophic if ignored. This study has considered the low, medium, high and very high prevalence scenarios as key parameters for the production of waste. In addition, the uncertainty in citizens' demand for waste collection was also included in the proposed model. The results showed that by energy production from waste during the COVID-19 pandemic, 34% of the total cost of collecting and transporting waste can be compensated. Finally, this paper obtained useful managerial insights using the data of Kermanshah city as a real case.

Predictors of Return to Work for Occupational Rehabilitation Users in Work-Related Injury Insurance Claims: Insights from Mental Health.

Purpose This study evaluates the Occupational Rehabilitation (OR) initiatives regarding return to work (RTW) and sustaining at work following work-related injuries. This study also identifies the predictors and predicts the likelihoods of RTW and sustainability for OR users. Methods The study is conducted on the compensation claim data for people who are injured at work in the state of Victoria, Australia. The claims which commenced OR services between the first of July 2012 and the end of June 2015 are included. The claims which used original employer services (OES) have been separated from claims which used new employer services (NES). We investigated a range of predictors categorised into four groups as claimant, injury, and employment characteristics and claim management. The RTW and sustaining at work are outcomes of interest. To evaluate the predictors, we use Chi-squared test and logistic regression modelling. Also, we prioritized the predictors using Akaike Information Criterion (AIC) measure and Cross-validation error. Four predictive models are developed using significant predictors for OES and NES users to predict RTW and sustainability. We examined the multicollinearity of the developed models using Variance Inflation Factor (VIF). Results About 75% and 60% of OES users achieved RTW and have been sustained at work respectively, whilst just approximately 30% of NES users have been placed at a new employer and 25% of them have been sustained at work. The predictors which have the most association with OES and NES outcomes are the use of psychiatric services and age groups respectively. We found that having mental conditions is as an important indicator to allocate injured workers into OES or NES initiatives. Our study shows that injured workers with mental issues do not always have lower RTW rate. They just need special consideration. Conclusion Understanding the predictors of RTW and sustainability helps to develop interventions to ensure sustained RTW. This study will assist decision makers to improve design and implementation of OR services and tailor services according to clients' needs.

A supervised adverse drug reaction signalling framework imitating Bradford Hill's causality considerations.

Big longitudinal observational medical data potentially hold a wealth of information and have been recognised as potential sources for gaining new drug safety knowledge. Unfortunately there are many complexities and underlying issues when analysing longitudinal observational data. Due to these complexities, existing methods for large-scale detection of negative side effects using observational data all tend to have issues distinguishing between association and causality. New methods that can better discriminate causal and non-causal relationships need to be developed to fully utilise the data. In this paper we propose using a set of causality considerations developed by the epidemiologist Bradford Hill as a basis for engineering features that enable the application of supervised learning for the problem of detecting negative side effects. The Bradford Hill considerations look at various perspectives of a drug and outcome relationship to determine whether it shows causal traits. We taught a classifier to find patterns within these perspectives and it learned to discriminate between association and causality. The novelty of this research is the combination of supervised learning and Bradford Hill's causality considerations to automate the Bradford Hill's causality assessment. We evaluated the framework on a drug safety gold standard known as the observational medical outcomes partnership's non-specified association reference set. The methodology obtained excellent discrimination ability with area under the curves ranging between 0.792 and 0.940 (existing method optimal: 0.73) and a mean average precision of 0.640 (existing method optimal: 0.141). The proposed features can be calculated efficiently and be readily updated, making the framework suitable for big observational data.

A review on over-sampling techniques in classification of multi-class imbalanced datasets: insights for medical problems.

There has been growing attention to multi-class classification problems, particularly those challenges of imbalanced class distributions. To address these challenges, various strategies, including data-level re-sampling treatment and ensemble methods, have been introduced to bolster the performance of predictive models and Artificial Intelligence (AI) algorithms in scenarios where excessive level of imbalance is present. While most research and algorithm development have been focused on binary classification problems, in health informatics there is an increased interest in the field to address the problem of multi-class classification in imbalanced datasets. Multi-class imbalance problems bring forth more complex challenges, as a delicate approach is required to generate synthetic data and simultaneously maintain the relationship between the multiple classes. The aim of this review paper is to examine over-sampling methods tailored for medical and other datasets with multi-class imbalance. Out of 2,076 peer-reviewed papers identified through searches, 197 eligible papers were chosen and thoroughly reviewed for inclusion, narrowing to 37 studies being selected for in-depth analysis. These studies are categorised into four categories: metric, adaptive, structure-based, and hybrid approaches. The most significant finding is the emerging trend toward hybrid resampling methods that combine the strengths of various techniques to effectively address the problem of imbalanced data. This paper provides an extensive analysis of each selected study, discusses their findings, and outlines directions for future research.

Optimised deep k-nearest neighbour's based diabetic retinopathy diagnosis(ODeep-NN) using retinal images.

Diabetes mellitus has been regarded as one of the prime health issues in present days, which can often lead to diabetic retinopathy, a complication of the disease that affects the eyes, causing loss of vision. For precisely detecting the condition's existence, clinicians are required to recognise the presence of lesions in colour fundus images, making it an arduous and time-consuming task. To deal with this problem, a lot of work has been undertaken to develop deep learning-based computer-aided diagnosis systems that assist clinicians in making accurate diagnoses of the diseases in medical images. Contrariwise, the basic operations involved in deep learning models lead to the extraction of a bulky set of features, further taking a long period of training to predict the existence of the disease. For effective execution of these models, feature selection becomes an important task that aids in selecting the most appropriate features, with an aim to increase the classification accuracy. This research presents an optimised deep k-nearest neighbours'-based pipeline model in a bid to amalgamate the feature extraction capability of deep learning models with nature-inspired metaheuristic algorithms, further using k-nearest neighbour algorithm for classification. The proposed model attains an accuracy of 97.67 and 98.05% on two different datasets considered, outperforming Resnet50 and AlexNet deep learning models. Additionally, the experimental results also portray an analysis of five different nature-inspired metaheuristic algorithms, considered for feature selection on the basis of various evaluation parameters.

Investigating mathematical models of immuno-interactions with early-stage cancer under an agent-based modelling perspective.

Many advances in research regarding immuno-interactions with cancer were developed with the help of ordinary differential equation (ODE) models. These models, however, are not effectively capable of representing problems involving individual localisation, memory and emerging properties, which are common characteristics of cells and molecules of the immune system. Agent-based modelling and simulation is an alternative paradigm to ODE models that overcomes these limitations. In this paper we investigate the potential contribution of agent-based modelling and simulation when compared to ODE modelling and simulation. We seek answers to the following questions: Is it possible to obtain an equivalent agent-based model from the ODE formulation? Do the outcomes differ? Are there any benefits of using one method compared to the other? To answer these questions, we have considered three case studies using established mathematical models of immune interactions with early-stage cancer. These case studies were re-conceptualised under an agent-based perspective and the simulation results were then compared with those from the ODE models. Our results show that it is possible to obtain equivalent agent-based models (i.e. implementing the same mechanisms); the simulation output of both types of models however might differ depending on the attributes of the system to be modelled. In some cases, additional insight from using agent-based modelling was obtained. Overall, we can confirm that agent-based modelling is a useful addition to the tool set of immunologists, as it has extra features that allow for simulations with characteristics that are closer to the biological phenomena.

Evaluation of the Early Intervention Physiotherapist Framework for Injured Workers in Victoria, Australia: Data Analysis Follow-Up.

PURPOSE: This study evaluates the performance of the Early Intervention Physiotherapist Framework (EIPF) for injured workers. This study provides a proper follow-up period (3 years) to examine the impacts of the EIPF program on injury outcomes such as return to work (RTW) and time to RTW. This study also identifies the factors influencing the outcomes. METHODS: The study was conducted on data collected from compensation claims of people who were injured at work in Victoria, Australia. Injured workers who commenced their compensation claims after the first of January 2010 and had their initial physiotherapy consultation after the first of August 2014 are included. To conduct the comparison, we divided the injured workers into two groups: physiotherapy services provided by EIPF-trained physiotherapists (EP) and regular physiotherapists (RP) over the three-year intervention period. We used three different statistical analysis methods to evaluate the performance of the EIPF program. We used descriptive statistics to compare two groups based on physiotherapy services and injury outcomes. We also completed survival analysis using Kaplan-Meier curves in terms of time to RTW. We developed univariate and multivariate regression models to investigate whether the difference in outcomes was achieved after adjusting for significantly associated variables. RESULTS: The results showed that physiotherapists in the EP group, on average, dealt with more claims (over twice as many) than those in the RP group. Time to RTW for the injured workers treated by the EP group was significantly lower than for those who were treated by the RP group, indicated by descriptive, survival, and regression analyses. Earlier intervention by physiotherapists led to earlier RTW. CONCLUSION: This evaluation showed that the EIPF program achieved successful injury outcomes three years after implementation. Motivating physiotherapists to intervene earlier in the recovery process of injured workers through initial consultation helps to improve injury outcomes.

Energy valley optimizer: a novel metaheuristic algorithm for global and engineering optimization.

In this paper, Energy Valley Optimizer (EVO) is proposed as a novel metaheuristic algorithm inspired by advanced physics principles regarding stability and different modes of particle decay. Twenty unconstrained mathematical test functions are utilized in different dimensions to evaluate the proposed algorithm's performance. For statistical purposes, 100 independent optimization runs are conducted to determine the statistical measurements, including the mean, standard deviation, and the required number of objective function evaluations, by considering a predefined stopping criterion. Some well-known statistical analyses are also used for comparative purposes, including the Kolmogorov-Smirnov, Wilcoxon, and Kruskal-Wallis analysis. Besides, the latest Competitions on Evolutionary Computation (CEC), regarding real-world optimization, are also considered for comparing the results of the EVO to the most successful state-of-the-art algorithms. The results demonstrate that the proposed algorithm can provide competitive and outstanding results in dealing with complex benchmarks and real-world problems.

Shape and size optimization of truss structures by Chaos game optimization considering frequency constraints.

INTRODUCTION: An engineering system consists of properly established activities and put together to achieve a predefined goal. These activities include analysis, design, construction, research, and development. Designing and constructing structural systems, including buildings, bridges, highways, and other complex systems, have been developed over the centuries. However, the evolution of these systems has been prolonged because the overall process is very costly and time-consuming, requiring primary human and material resources to be utilized. One of the options for overcoming these shortcomings is the utilization of metaheuristic algorithms as recently developed intelligent techniques. These algorithms can be utilized as upper-level search techniques for optimization procedures to achieve better results. OBJECTIVES: Shape and size optimization of truss structures are considered in this paper utilizing the Chaos Game Optimization (CGO) as one of the recently developed metaheuristic algorithms. The principles of chaos theory and fractal configuration are considered inspirational concepts. METHODS: For the numerical purpose, the 10-bar, 37-bar, 52-bar, 72-bar, and 120-bar truss structures as four of the benchmark problems in this field are considered as design examples in which the frequency constraints are considered as limits that have to be dealt with during the optimization procedure. Multiple optimization runs are also conducted for having a comprehensive statistical analysis, while a comparative investigation is also conducted with other algorithms in the literature. RESULTS: Based on the results of the CGO and other approaches from the literature, the CGO can provide better and competitive results in dealing with the considered truss design problems. CONCLUSION: In summary, the CGO can provide better solutions in dealing with the considered real-size structural design problems with higher levels of complexity.

Novel dynamic fuzzy Decision-Making framework for COVID-19 vaccine dose recipients.

Introduction: The vaccine distribution for the COVID-19 is a multicriteria decision-making (MCDM) problem based on three issues, namely, identification of different distribution criteria, importance criteria and data variation. Thus, the Pythagorean fuzzy decision by opinion score method (PFDOSM) for prioritising vaccine recipients is the correct approach because it utilises the most powerful MCDM ranking method. However, PFDOSM weighs the criteria values of each alternative implicitly, which is limited to explicitly weighting each criterion. In view of solving this theoretical issue, the fuzzy-weighted zero-inconsistency (FWZIC) can be used as a powerful weighting MCDM method to provide explicit weights for a criteria set with zero inconstancy. However, FWZIC is based on the triangular fuzzy number that is limited in solving the vagueness related to the aforementioned theoretical issues. Objectives: This research presents a novel homogeneous Pythagorean fuzzy framework for distributing the COVID-19 vaccine dose by integrating a new formulation of the PFWZIC and PFDOSM methods. Methods: The methodology is divided into two phases. Firstly, an augmented dataset was generated that included 300 recipients based on five COVID-19 vaccine distribution criteria (i.e., vaccine recipient memberships, chronic disease conditions, age, geographic location severity and disabilities). Then, a decision matrix was constructed on the basis of an intersection of the 'recipients list' and 'COVID-19 distribution criteria'. Then, the MCDM methods were integrated. An extended PFWZIC was developed, followed by the development of PFDOSM. Results: (1) PFWZIC effectively weighted the vaccine distribution criteria. (2) The PFDOSM-based group prioritisation was considered in the final distribution result. (3) The prioritisation ranks of the vaccine recipients were subject to a systematic ranking that is supported by high correlation results over nine scenarios of the changing criteria weights values. Conclusion: The findings of this study are expected to ensuring equitable protection against COVID-19 and thus help accelerate vaccine progress worldwide.

A Review on Human-AI Interaction in Machine Learning and Insights for Medical Applications.

OBJECTIVE: To provide a human-Artificial Intelligence (AI) interaction review for Machine Learning (ML) applications to inform how to best combine both human domain expertise and computational power of ML methods. The review focuses on the medical field, as the medical ML application literature highlights a special necessity of medical experts collaborating with ML approaches. METHODS: A scoping literature review is performed on Scopus and Google Scholar using the terms "human in the loop", "human in the loop machine learning", and "interactive machine learning". Peer-reviewed papers published from 2015 to 2020 are included in our review. RESULTS: We design four questions to investigate and describe human-AI interaction in ML applications. These questions are "Why should humans be in the loop?", "Where does human-AI interaction occur in the ML processes?", "Who are the humans in the loop?", and "How do humans interact with ML in Human-In-the-Loop ML (HILML)?". To answer the first question, we describe three main reasons regarding the importance of human involvement in ML applications. To address the second question, human-AI interaction is investigated in three main algorithmic stages: 1. data producing and pre-processing; 2. ML modelling; and 3. ML evaluation and refinement. The importance of the expertise level of the humans in human-AI interaction is described to answer the third question. The number of human interactions in HILML is grouped into three categories to address the fourth question. We conclude the paper by offering a discussion on open opportunities for future research in HILML.

Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy.

A substantial impediment to widespread Coronavirus disease (COVID-19) vaccination is vaccine hesitancy. Many researchers across scientific disciplines have presented countless studies in favor of COVID-19 vaccination, but misinformation on social media could hinder vaccination efforts and increase vaccine hesitancy. Nevertheless, studying people's perceptions on social media to understand their sentiment presents a powerful medium for researchers to identify the causes of vaccine hesitancy and therefore develop appropriate public health messages and interventions. To the best of the authors' knowledge, previous studies have presented vaccine hesitancy in specific cases or within one scientific discipline (i.e., social, medical, and technological). No previous study has presented findings via sentiment analysis for multiple scientific disciplines as follows: (1) social, (2) medical, public health, and (3) technology sciences. Therefore, this research aimed to review and analyze articles related to different vaccine hesitancy cases in the last 11 years and understand the application of sentiment analysis on the most important literature findings. Articles were systematically searched in Web of Science, Scopus, PubMed, IEEEXplore, ScienceDirect, and Ovid from January 1, 2010, to July 2021. A total of 30 articles were selected on the basis of inclusion and exclusion criteria. These articles were formed into a taxonomy of literature, along with challenges, motivations, and recommendations for social, medical, and public health and technology sciences. Significant patterns were identified, and opportunities were promoted towards the understanding of this phenomenon.

Based on T-spherical fuzzy environment: A combination of FWZIC and FDOSM for prioritising COVID-19 vaccine dose recipients.

The problem complexity of multi-criteria decision-making (MCDM) has been raised in the distribution of coronavirus disease 2019 (COVID-19) vaccines, which required solid and robust MCDM methods. Compared with other MCDM methods, the fuzzy-weighted zero-inconsistency (FWZIC) method and fuzzy decision by opinion score method (FDOSM) have demonstrated their solidity in solving different MCDM challenges. However, the fuzzy sets used in these methods have neglected the refusal concept and limited the restrictions on their constants. To end this, considering the advantage of the T-spherical fuzzy sets (T-SFSs) in handling the uncertainty in the data and obtaining information with more degree of freedom, this study has extended FWZIC and FDOSM methods into the T-SFSs environment (called T-SFWZIC and T-SFDOSM) to be used in the distribution of COVID-19 vaccines. The methodology was formulated on the basis of decision matrix adoption and development phases. The first phase described the adopted decision matrix used in the COVID-19 vaccine distribution. The second phase presented the sequential formulation steps of T-SFWZIC used for weighting the distribution criteria followed by T-SFDOSM utilised for prioritising the vaccine recipients. Results revealed the following: (1) T-SFWZIC effectively weighted the vaccine distribution criteria based on several parameters including T = 2, T = 4, T = 6, T = 8, and T = 10. Amongst all parameters, the age criterion received the highest weight, whereas the geographic locations severity criterion has the lowest weight. (2) According to the T parameters, a considerable variance has occurred on the vaccine recipient orders, indicating that the existence of T values affected the vaccine distribution. (3) In the individual context of T-SFDOSM, no unique prioritisation was observed based on the obtained opinions of each expert. (4) The group context of T-SFDOSM used in the prioritisation of vaccine recipients was considered the final distribution result as it unified the differences found in an individual context. The evaluation was performed based on systematic ranking assessment and sensitivity analysis. This evaluation showed that the prioritisation results based on each T parameter were subject to a systematic ranking that is supported by high correlation results over all discussed scenarios of changing criteria weights values.

Tailored RF pulse for magnetization inversion at ultrahigh field.

The radiofrequency (RF) transmit field is severely inhomogeneous at ultrahigh field due to both RF penetration and RF coil design issues. This particularly impairs image quality for sequences that use inversion pulses such as magnetization prepared rapid acquisition gradient echo and limits the use of quantitative arterial spin labeling sequences such as flow-attenuated inversion recovery. Here we have used a search algorithm to produce inversion pulses tailored to take into account the heterogeneity of the RF transmit field at 7 T. This created a slice selective inversion pulse that worked well (good slice profile and uniform inversion) over the range of RF amplitudes typically obtained in the head at 7 T while still maintaining an experimentally achievable pulse length and pulse amplitude in the brain at 7 T. The pulses used were based on the frequency offset correction inversion technique, as well as time dilation of functions, but the RF amplitude, frequency sweep, and gradient functions were all generated using a genetic algorithm with an evaluation function that took into account both the desired inversion profile and the transmit field inhomogeneity.

Early Identification of Undesirable Outcomes for Transport Accident Injured Patients Using Semi-Supervised Clustering.

Identifying those patient groups, who have unwanted outcomes, in the early stages is crucial to providing the most appropriate level of care. In this study, we intend to find distinctive patterns in health service use (HSU) of transport accident injured patients within the first week post-injury. Aiming those patterns that are associated with the outcome of interest. To recognize these patterns, we propose a multi-objective optimization model that minimizes the k-medians cost function and regression error simultaneously. Thus, we use a semi-supervised clustering approach to identify patient groups based on HSU patterns and their association with total cost. To solve the optimization problem, we introduce an evolutionary algorithm using stochastic gradient descent and Pareto optimal solutions. As a result, we find the best optimal clusters by minimizing both objective functions. The results show that the proposed semi-supervised approach identifies distinct groups of HSUs and contributes to predict total cost. Also, the experiments prove the performance of the multi-objective approach in comparison with single- objective approaches.

Multi-objective semi-supervised clustering to identify health service patterns for injured patients.

PURPOSE: This study develops a pattern recognition method that identifies patterns based on their similarity and their association with the outcome of interest. The practical purpose of developing this pattern recognition method is to group patients, who are injured in transport accidents, in the early stages post-injury. This grouping is based on distinctive patterns in health service use within the first week post-injury. The groups also provide predictive information towards the total cost of medication process. As a result, the group of patients who have undesirable outcomes are identified as early as possible based health service use patterns. METHODS: We propose a multi-objective optimization model to group patients. An objective function is the cost function of k-medians clustering to recognize the similar patterns. Another objective function is the cross-validated root-mean-square error to examine the association with the total cost. The best grouping is obtained by minimizing both objective functions. As a result, the multi-objective optimization model is a semi-supervised clustering which learns health service use patterns in both unsupervised and supervised ways. We also introduce an evolutionary computation approach includes stochastic gradient descent and Pareto optimal solutions to find the optimal solution. In addition, we use the decision tree method to reproduce the optimal groups using an interpretable classification model. RESULTS: The results show that the proposed multi-objective semi-supervised clustering identifies distinct groups of health service uses and contributes to predict the total cost. The performance of the multi-objective model has been examined using two metrics such as the average silhouette width and the cross-validation error. The examination proves that the multi-objective model outperforms the single-objective ones. In addition, the interpretable classification model shows that imaging and therapeutic services are critical services in the first-week post-injury to group injured patients. CONCLUSION: The proposed multi-objective semi-supervised clustering finds the optimal clusters that not only are well-separated from each other but can provide informative insights regarding the outcome of interest. It also overcomes two drawback of clustering methods such as being sensitive to the initial cluster centers and need for specifying the number of clusters.

Imputation techniques on missing values in breast cancer treatment and fertility data.

Clinical decision support using data mining techniques offers more intelligent way to reduce the decision error in the last few years. However, clinical datasets often suffer from high missingness, which adversely impacts the quality of modelling if handled improperly. Imputing missing values provides an opportunity to resolve the issue. Conventional imputation methods adopt simple statistical analysis, such as mean imputation or discarding missing cases, which have many limitations and thus degrade the performance of learning. This study examines a series of machine learning based imputation methods and suggests an efficient approach to in preparing a good quality breast cancer (BC) dataset, to find the relationship between BC treatment and chemotherapy-related amenorrhoea, where the performance is evaluated with the accuracy of the prediction. To this end, the reliability and robustness of six well-known imputation methods are evaluated. Our results show that imputation leads to a significant boost in the classification performance compared to the model prediction based on listwise deletion. Furthermore, the results reveal that most methods gain strong robustness and discriminant power even the dataset experiences high missing rate (> 50%).