Mehrbakhsh Nilashi

Role of OpenEHR as an open source solution for the regional modelling of patient data in obstetrics

This work investigates, whether openEHR with its reference model, archetypes and templates is suitable for the digital representation of demographic as well as clinical data. Moreover, it elaborates openEHR as a tool for modelling Hospital Information Systems on a regional level based on a national logical infrastructure. OpenEHR is a dual model approach developed for the modelling of Hospital Information Systems enabling semantic interoperability. A holistic solution to this represents the use of dual model based Electronic Healthcare Record systems. Modelling data in the field of obstetrics is a challenge, since different regions demand locally specific information for the process of treatment. Smaller health units in developing countries like Brazil or Malaysia, which until recently handled automatable processes like the storage of sensitive patient data in paper form, start organizational reconstruction processes. This archetype proof-of-concept investigation has tried out some elements of the openEHR methodology in cooperation with a health unit in Colombo, Brazil. Two legal forms provided by the Brazilian Ministry of Health have been analyzed and classified into demographic and clinical data. LinkEHR-Ed editor was used to read, edit and create archetypes. Results show that 33 clinical and demographic concepts, which are necessary to cover data demanded by the Unified National Health System, were identified. Out of the concepts 61% were reused and 39% modified to cover domain requirements. The detailed process of reuse, modification and creation of archetypes is shown. We conclude that, although a major part of demographic and clinical patient data were already represented by existing archetypes, a significant part required major modifications. In this study openEHR proved to be a highly suitable tool in the modelling of complex health data. In combination with LinkEHR-Ed software it offers user-friendly and highly applicable tools, although the complexity built by the vast specifications requires expert networks to define generally excepted clinical models. Finally, this project has pointed out main benefits enclosing high coverage of obstetrics data on the Clinical Knowledge Manager, simple modelling, and wide network and support using openEHR. Moreover, barriers described are enclosing the allocation of clinical content to respective archetypes, as well as stagnant adaption of changes on the Clinical Knowledge Manager leading to redundant efforts in data contribution that need to be addressed in future works.

A systematic review and meta-Analysis of SWARA and WASPAS methods: Theory and applications with recent fuzzy developments

The Multiple Criteria Decision Making (MCDM) utility determining approaches and fuzzy sets are considered to be new development approaches, which have been recently presented, extended, and used by some scholars in area of decision making. There is a lack of research regarding to systematic literature review and classification of study about these approaches. Therefore; in the present study, the attempt is made to present a systematic review of methodologies and applications with recent fuzzy developments of two new MCDM utility determining approaches including Step-wise Weight Assessment Ratio Analysis (SWARA) and the Weighted Aggregated Sum Product Assessment (WASPAS) and fuzzy extensions which discussed in recent years. Regarding this, some major databases including Web of Science, Scopus and Google Scholar have been nominated and systematic and meta-analysis method which called “PRISMA” has been proposed. In addition, the selected articles were classified based on authors, the year of publication, journals and conferences names, the technique and method used, research objectives, research gap and problem, solution and modeling, and finally results and findings. The results of this study can assist decision-makers in handling information such as stakeholders’ preferences, interconnected or contradictory criteria and uncertain environments. In addition, findings of this study help to practitioners and academic for adopting the new MCDM utility techniques such as WASPAS and SWARA in different application areas and presenting insight into literature.

Accuracy Improvement for Predicting Parkinson’s Disease Progression

Parkinson’s disease (PD) is a member of a larger group of neuromotor diseases marked by the progressive death of dopamineproducing cells in the brain. Providing computational tools for Parkinson disease using a set of data that contains medical information is very desirable for alleviating the symptoms that can help the amount of people who want to discover the risk of disease at an early stage. This paper proposes a new hybrid intelligent system for the prediction of PD progression using noise removal, clustering and prediction methods. Principal Component Analysis (PCA) and Expectation Maximization (EM) are respectively employed to address the multi-collinearity problems in the experimental datasets and clustering the data. We then apply Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) for prediction of PD progression. Experimental results on public Parkinson’s datasets show that the proposed method remarkably improves the accuracy of prediction of PD progression. The hybrid intelligent system can assist medical practitioners in the healthcare practice for early detection of Parkinson disease.

Recommendation quality, transparency, and website quality for trust-building in recommendation agents

A new model for trust-building in recommendation agents is proposed.We consider recommendation quality, transparency, and website quality in the model.We conducted empirical studies in the context of two popular e-commerce websites.The results highlight the importance of perceived transparency for trust-building. Trust is a main success factor for automated recommendation agents on e-commerce sites. Various aspects can contribute to the development of trust toward such an agent, including perceptions about the usefulness of the recommendations, the transparency of the recommendation process, and the general quality of the website. These factors have been analyzed in isolation in the literature though. We propose and evaluate a new trust model that integrates these factors, and allows us to assess their relative importance for trust-building. We conducted empirical studies in the context of two popular e-commerce websites. The findings suggest that transparency is equally important to consumers for building trust as recommendation quality, and that general we site quality contributes to the development of trust. The findings indicate that focusing on recommendation quality may be insufficient and higher levels of adoption of the recommendations can be achieved when several trust-building factors are considered.

A knowledge-based system for breast cancer classification using fuzzy logic method

A knowledge-based system is proposed for breast cancer disease classification.The knowledge-based system uses EM, PCA, CART and fuzzy rule-based methods.WDBC and Mammographic mass datasets are used for the method evaluation.The accuracies obtained by the method are respectively 0.932 and 0.941 for WDBC and Mammographic mass datasets. Breast cancer has become a common disease around the world. Expert systems are valuable tools that have been successful for the disease diagnosis. In this research, we accordingly develop a new knowledge-based system for classification of breast cancer disease using clustering, noise removal, and classification techniques. Expectation Maximization (EM) is used as a clustering method to cluster the data in similar groups. We then use Classification and Regression Trees (CART) to generate the fuzzy rules to be used for the classification of breast cancer disease in the knowledge-based system of fuzzy rule-based reasoning method. To overcome the multi-collinearity issue, we incorporate Principal Component Analysis (PCA) in the proposed knowledge-based system. Experimental results on Wisconsin Diagnostic Breast Cancer and Mammographic mass datasets show that proposed methods remarkably improves the prediction accuracy of breast cancer. The proposed knowledge-based system can be used as a clinical decision support system to assist medical practitioners in the healthcare practice.

A recommender system for tourism industry using cluster ensemble and prediction machine learning techniques

Recommender systems have emerged in the e-commerce domain and are developed to actively recommend the right items to online users. Traditional Collaborative Filtering (CF) recommender systems recommend the items to users based on their single-rating feedback which are used to match similar users. In multi-criteria CF recommender systems, however, multi-criteria ratings are used instead of single-rating feedback which can significantly improve the accuracy of traditional CF algorithms. These systems have been successfully implemented in Tourism domain. In this paper, we propose a new recommendation method based on multi-criteria CF to enhance the predictive accuracy of recommender systems in tourism domain using clustering, dimensionality reduction and prediction methods. We use Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Support Vector Regression (SVR) as prediction techniques, Principal Component Analysis (PCA) as a dimensionality reduction technique and Self-Organizing Map (SOM) and Expectation Maximization (EM) as two well-known clustering techniques. To improve the recommendation accuracy of proposed multi-criteria CF, a cluster ensembles approach, Hypergraph Partitioning Algorithm (HGPA), is applied on SOM and EM clustering results. We evaluate the accuracy of recommendation method on TripAdvisior dataset. Our experiments confirm that cluster ensembles can provide better predictive accuracy for the proposed recommendation method in relation to the methods which solely rely on single clustering techniques.

Decision Making Methods Based on Fuzzy Aggregation Operators: Three Decades Review from 1986 to 2017

In many real-life decision making (DM) situations, the available information is vague or imprecise. To adequately solve decision problems with vague or imprecise information, fuzzy set theory and aggregation operator theory have become powerful tools. In last three decades, DM theories and methods under fuzzy aggregation operator have been proposed and developed for effectively solving the DM problems and numerous applications have been reported in the literature. While various aggregation operators have been suggested and developed, there is a lack of research regarding systematic literature review and classification of study in this field. Regarding this, Web of Science database has been nominated and systematic and meta-analysis method called “PRISMA” has been proposed. Accordingly, a review of 312 published articles appearing in 33 popular journals related to fuzzy set theory, aggregation operator theory and DM approaches between July 1986 and June 2017 have been attained to reach a comprehensive review of DM methods and aggregation operator environment. Consequently, the selected published articles have been categorized by name of author(s), the publication year, technique, application area, country, research contribution and journals in which they appeared. The findings of this study found that, ordered weighted averaging (OWA) has been the highest frequently accessed more than other areas. This systematic review shows that the DM theories under fuzzy aggregation operator environment have received a great deal of interest from researchers and practitioners in many disciplines.

A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques

A new method is developed for recommender systems.The recommender system is developed based on collaborative filtering.Scalability and sparsity issues in recommender systems are solved.MovieLens and Yahoo! Webscope R4 datasets are used for method evaluation.The method is effective in solving the sparsity and scalability problems in CF. Improving the efficiency of methods has been a big challenge in recommender systems. It has been also important to consider the trade-off between the accuracy and the computation time in recommending the items by the recommender systems as they need to produce the recommendations accurately and meanwhile in real-time. In this regard, this research develops a new hybrid recommendation method based on Collaborative Filtering (CF) approaches. Accordingly, in this research we solve two main drawbacks of recommender systems, sparsity and scalability, using dimensionality reduction and ontology techniques. Then, we use ontology to improve the accuracy of recommendations in CF part. In the CF part, we also use a dimensionality reduction technique, Singular Value Decomposition(SVD), to find the most similar items and users in each cluster of items and users which can significantly improve the scalability of the recommendation method. We evaluate the method on two real-world datasets to show its effectiveness and compare the results with the results of methods in the literature. The results showed that our method is effective in improving the sparsity and scalability problems in CF.

An analytical method for diseases prediction using machine learning techniques

Abstract The use of medical datasets has attracted the attention of researchers worldwide. Data mining techniques have been widely used in developing decision support systems for diseases prediction through a set of medical datasets. In this paper, we propose a new knowledge-based system for diseases prediction using clustering, noise removal, and prediction techniques. We use Classification and Regression Trees (CART) to generate the fuzzy rules to be used in the knowledge-based system. We test our proposed method on several public medical datasets. Results on Pima Indian Diabetes, Mesothelioma, WDBC, StatLog, Cleveland and Parkinson’s telemonitoring datasets show that proposed method remarkably improves the diseases prediction accuracy. The results showed that the combination of fuzzy rule-based, CART with noise removal and clustering techniques can be effective in diseases prediction from real-world medical datasets. The knowledge-based system can assist medical practitioners in the healthcare practice as a clinical analytical method.

Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

Rough set theory has been used extensively in fields of complexity, cognitive sciences, and artificial intelligence, especially in numerous fields such as expert systems, knowledge discovery, information system, inductive reasoning, intelligent systems, data mining, pattern recognition, decision-making, and machine learning. Rough sets models, which have been recently proposed, are developed applying the different fuzzy generalisations. Currently, there is not a systematic literature review and classification of these new generalisations about rough set models. Therefore, in this review study, the attempt is made to provide a comprehensive systematic review of methodologies and applications of recent generalisations discussed in the area of fuzzy-rough set theory. On this subject, the Web of Science database has been chosen to select the relevant papers. Accordingly, the systematic and meta-analysis approach, which is called “PRISMA,” has been proposed and the selected articles were classified based on the author and year of publication, author nationalities, application field, type of study, study category, study contribution, and journal in which the articles have appeared. Based on the results of this review, we found that there are many challenging issues related to the different application area of fuzzy-rough set theory which can motivate future research studies.