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This paper aims to classify journal papers in the context of hybrid quality function deployment QFD and multi-criteria decision-making (MCDM) methods published during 2004–2021.

A conceptual classification scheme is presented to analyze the hybrid QFD-MCDM methods. Then some recommendations are given to introduce directions for future research.

The results show that among all related areas, the manufacturing application has the most frequency of published papers regarding hybrid QFD-MCDM methods. Moreover, using uncertainty to establish a hybrid QFD-MCDM the relevant papers have been considered during the time interval 2004–2021.

There are various shortcomings in conventional QFD which limit its efficiency and potential applications. Since 2004, when MCDM methods were frequently adopted in the quality management context, increasing attention has been drawn from both practical and academic perspectives. Recently, the integration of MCDM techniques into the QFD model has played an important role in designing new products and services, supplier selection, green manufacturing systems and sustainability topics. Hence, this survey reviewed hybrid QFD-MCDM methods during 2004–2021.

Heart diseases have become one of the most causes of death among Egyptians. With 500 deaths per 100,000 occurring annually in Egypt, it has been noticed that medical data faces a high-dimensional problem that leads to a decrease in the classification accuracy of heart data. So the purpose of this study is to improve the classification accuracy of heart disease data for helping doctors efficiently diagnose heart disease by using a hybrid classification technique.

This paper used a new approach based on the integration between dimensionality reduction techniques as multiple correspondence analysis (MCA) and principal component analysis (PCA) with fuzzy c means (FCM) then with both of multilayer perceptron (MLP) and radial basis function networks (RBFN) which separate patients into different categories based on their diagnosis results in this paper, a comparative study of the performance performed including six structures such as MLP, RBFN, MLP via FCM–MCA, MLP via FCM–PCA, RBFN via FCM–MCA and RBFN via FCM–PCA to reach to the best classifier.

The results show that the MLP via FCM–MCA classifier structure has the highest ratio of classification accuracy and has the best performance superior to other methods; and that Smoking was the most factor causing heart disease.

This paper shows the importance of integrating statistical methods in increasing the classification accuracy of heart disease data.

Considering the human factor, the quality of the personnel is vital to ensure especially the value creation in the ports. Therefore, employee quality stands out for withstanding the pressures that stem from global trade on its operational speed felt by ports in recent years. Accordingly, the selection of the qualified personnel at the ports is very critical and a tool based on dynamic capabilities is needed to manage this process well. The aim of this study is to develop a model based on dynamic capabilities for recruitment process of ports.

Port personnel should have dynamic capabilities detected from the literature. These capabilities were approached as criteria. In this study, Buckley's proposed fuzzy analytical hierarchy process (AHP) method was employed for weighting the whole criteria. After that, weights of the criteria were used to prioritize alternatives with the fuzzy TOPSIS method.

This model reflects port managers' priorities and port customers' evaluations. Thus, the model can also reflect the level of integration of ports' related department managers into the recruitment process. The analyses allow the evaluation of the attitudes of the human resources department in the related port while fulfilling the personnel recruitment function. As a result of analyses, differences between perceptions of port managers and customers served as a feedback to the human resource management department of the ports.

One of the originalities of this study was derived from its customer-oriented perspective. This is a unique study that gathers common personnel capabilities related to the operation, planning and customer relationship departments and evaluates the success of these capabilities from the customer perspective.

Governments around the world instituted guidelines for calculating energy efficiency of vehicles not only by models, but by the whole universe of new vehicles registered. This paper compiles Multi-criteria decision-making (MCDM) studies related to automotive industry. We applied a Systematic Literature Review on MCDM studies published until 2015 to identify patterns on MCDM applications to design vehicles more fuel efficient in order to achieve full compliance with energy efficiency guidelines (e.g., Inovar-Auto). From 339 papers, 45 papers have been identified as describing some MCDM technique and correlation to automotive industry. We classified the most common MCDM technique and application in the automotive industry. Integrated approaches were more usual than individual ones. Application of fuzzy methods to tackle uncertainties in the data was also observed. Despite the maturity in the use of MCDM in several areas of knowledge, and intensive use in the automotive industry, none of them are directly linked to car design for energy efficiency. Analytic Hierarchy Process was identified as the common technique applied in the automotive industry.

The study aimed to evaluate the effectiveness of using large data sets for new diabetes patient prescriptions.

This study consisted of 101,766 individuals, who had applied to the hospital with a diabetes diagnosis and were hospitalized for 1–14 days and subjected to laboratory tests and medication.

With the help of Mahout and Scala, data mining methods of random forest and multilayer perceptron were used. Accuracy rates of these methods were found to be 0.879 and 0.849 for Mahout and 0.849 and 0.870 for Scala.

The mahout random forest method provided a better prediction of new prescription requirements than the other methods according to accuracy criteria.