Missing data techniques in classification for cardiovascular dysautonomias diagnosis.

Missing data (MD) is a common and inevitable problem facing data mining (DM)-based decision systems in e-health since many medical historical datasets contain a huge number of missing values. Therefore, a pre-processing stage is usually required to deal with missing values before building any DM-based decision system. The purpose of this paper is to evaluate the impact of MD techniques on classification systems in cardiovascular dysautonomias diagnosis. We analyzed and compared the accuracy rates of four classification techniques: random forest (RF), support vector machines (SVM), C4.5 decision tree, and Naive Bayes (NB), using two MD techniques: deletion or imputation with k-nearest neighbors (KNN). A total of 216 experiments were therefore carried out using three missingness mechanisms (MCAR: missing completely at random, MAR: missing at random and NMAR: not missing at random), two MD techniques (deletion and KNN imputation), nine MD percentages from 10 to 90% over a dataset collected from the autonomic nervous system (ANS) unit of the University Hospital Avicenne in Morocco. The results obtained suggest that using KNN imputation rather than deletion enhances the accuracy rates of the four classifiers. Moreover, the MD percentages have a negative impact on the performance of classification techniques regardless of the MD mechanisms and MD techniques used. In fact, the accuracy rates of the four classifiers decrease as the MD percentage increases. Graphical abstract.

A mapping study of ensemble classification methods in lung cancer decision support systems.

Achieving a high level of classification accuracy in medical datasets is a capital need for researchers to provide effective decision systems to assist doctors in work. In many domains of artificial intelligence, ensemble classification methods are able to improve the performance of single classifiers. This paper reports the state of the art of ensemble classification methods in lung cancer detection. We have performed a systematic mapping study to identify the most interesting papers concerning this topic. A total of 65 papers published between 2000 and 2018 were selected after an automatic search in four digital libraries and a careful selection process. As a result, it was observed that diagnosis was the task most commonly studied; homogeneous ensembles and decision trees were the most frequently adopted for constructing ensembles; and the majority voting rule was the predominant combination rule. Few studies considered the parameter tuning of the techniques used. These findings open several perspectives for researchers to enhance lung cancer research by addressing the identified gaps, such as investigating different classification methods, proposing other heterogeneous ensemble methods, and using new combination rules. Graphical abstract Main features of the mapping study performed in ensemble classification methods applied on lung cancer decision support systems.

Mobile health applications for postnatal care: Review and analysis of functionalities and technical features.

BACKGROUND: Providing a continuum of care from antenatal, childbirth and postnatal period results in reduced maternal and neonatal morbidity and mortality. Timely, high quality postnatal care is crucial for maximizing maternal and newborn health. In this vein, the use of postnatal mobile applications constitutes a promising strategy. METHODS: A Systematic Literature Review (SLR) protocol was adopted to perform the selection, data extraction and functional evaluation of the available postnatal apps on iOS and Android platforms. The analysis of the functionalities and technical features of the apps selected was performed according to a 37-items assessment questionnaire developed on the basis of the scientific literature of postnatal care and a preliminary analysis of available postnatal apps RESULTS: A total of 48 postnatal apps were retrieved from the app repositories of the iOS and Android platforms. The results of the functional content analysis show that the postnatal apps selected relatively achieved low scores owing to the complexity and the ramification of the postnatal care. CONCLUSIONS: The present study helps in identifying areas related to the postnatal care that require further endeavors to be properly addressed. It also provides directions for developers to leverage the advancement and innovation on mobile technology to build complete and well-suited postnatal apps.

Applying trans-theoretical model for blood donation among Spanish adults: a cross-sectional study.

BACKGROUND: Relying solely on altruistic appeals may fail to fulfil the increasing demand for blood supplies. Current research has largely been attempted to determine and understand motives that serve as blood donation drivers. The Trans-Theoretical Model of behaviour change (TTM) can be used to conceptualise the process of intentional blood donation behaviour. METHODS: A cross sectional survey of Spanish adults was conducted. The final sample consisted of 504 individuals who were administered a self-report questionnaire including the measures of demographic characteristics, Stages of Change, Processes of Change, Self-efficacy and Decisional Balance. Data were analysed by frequency analysis, MANOVA/ANOVA and correlation analysis. RESULTS: Findings indicated that most of the behavioural and cognitive processes of change, self-efficacy and physical cons differentiated participants across the stages of change of blood donation. In contrast, eligibility cons and pros were less influential in stage transitions. Furthermore, significant correlations were observed between TTM constructs except for the physical cons and the processes of change. CONCLUSIONS: The present study extensively supports and replicates the applicability of the TTM to blood donation behaviour change and offers important implications for the development of effective stage-matched interventions to increase blood donation.

Sizing Prenatal mPHRs using COSMIC Measurement Method.

This paper presents an empirical evaluation of the COSMIC Function Points method (e.g., ISO 19761) through measuring the functional size of 33 prenatal mobile Personal Health Records (mPHRs) apps. This evaluation compares the functional size of each mobile app measured using the COSMIC method to the score of the app obtained in a previous evaluation that relied on functions extraction using a quality assessment questionnaire. It includes as well an investigation of the relationships between the functional sizes of these apps, their ratings in the apps stores, as well as the number of installs. As results, it was noticed that there is a considerable shift between the rankings of the functional sizes and the functionality scores obtained in the opinion-based questionnaire, for most of the apps assessed. Moreover, the study of the relationship between the functional sizes and the ratings, as well as the number of installs indicated that these variables are not linked, since they are impacted by external factors. The findings support the use of the COSMIC method for these apps in regard to measuring the functional size for further updates or improvements, which can also help developers to have an overview about the existing apps on the market and compare between them. Moreover, COSMIC is more effective since it covers all the features and functionalities of prenatal mPHRs.

Reviewing ensemble classification methods in breast cancer.

CONTEXT: Ensemble methods consist of combining more than one single technique to solve the same task. This approach was designed to overcome the weaknesses of single techniques and consolidate their strengths. Ensemble methods are now widely used to carry out prediction tasks (e.g. classification and regression) in several fields, including that of bioinformatics. Researchers have particularly begun to employ ensemble techniques to improve research into breast cancer, as this is the most frequent type of cancer and accounts for most of the deaths among women. OBJECTIVE AND METHOD: The goal of this study is to analyse the state of the art in ensemble classification methods when applied to breast cancer as regards 9 aspects: publication venues, medical tasks tackled, empirical and research types adopted, types of ensembles proposed, single techniques used to construct the ensembles, validation framework adopted to evaluate the proposed ensembles, tools used to build the ensembles, and optimization methods used for the single techniques. This paper was undertaken as a systematic mapping study. RESULTS: A total of 193 papers that were published from the year 2000 onwards, were selected from four online databases: IEEE Xplore, ACM digital library, Scopus and PubMed. This study found that of the six medical tasks that exist, the diagnosis medical task was that most frequently researched, and that the experiment-based empirical type and evaluation-based research type were the most dominant approaches adopted in the selected studies. The homogeneous type was that most widely used to perform the classification task. With regard to single techniques, this mapping study found that decision trees, support vector machines and artificial neural networks were those most frequently adopted to build ensemble classifiers. In the case of the evaluation framework, the Wisconsin Breast Cancer dataset was the most frequently used by researchers to perform their experiments, while the most noticeable validation method was k-fold cross-validation. Several tools are available to perform experiments related to ensemble classification methods, such as Weka and R Software. Few researchers took into account the optimisation of the single technique of which their proposed ensemble was composed, while the grid search method was that most frequently adopted to tune the parameter settings of a single classifier. CONCLUSION: This paper reports an in-depth study of the application of ensemble methods as regards breast cancer. Our results show that there are several gaps and issues and we, therefore, provide researchers in the field of breast cancer research with recommendations. Moreover, after analysing the papers found in this systematic mapping study, we discovered that the majority report positive results concerning the accuracy of ensemble classifiers when compared to the single classifiers. In order to aggregate the evidence reported in literature, it will, therefore, be necessary to perform a systematic literature review and meta-analysis in which an in-depth analysis could be conducted so as to confirm the superiority of ensemble classifiers over the classical techniques.

Design and Development of a Mobile App for Accessible Beach Tourism Information for People with Disabilities.

The global increase in the proportion of the population with disabilities has caused a greater awareness toward guaranteeing their use of public services. In particular, there is emphasis on the accessibility and inclusivity of tourism resources, to improve the enjoyment and well-being for people with motor disabilities. This paper presents a case study on accessibility to beaches in the Region of Murcia, Spain, which is one of the main tourist areas in the country. First, the most important elements that allow for the accessible use of beaches are analyzed and exposed in detail. Then, an extensive field-work in the area of interest has been carried out and its results are evaluated. Finally, the development of a new mobile app is described. The objective of this tool is to provide updated, accurate, and reliable accessibility information regarding the beaches. As a result, more than a third of the beaches analyzed had a high level of accessibility, while almost another third are totally inaccessible. The proposed application is a valuable tool, not only to help people with physical and motor disabilities, but also to raise awareness among local authorities to create and improve accessible services.

Sustainable Accessibility: A Mobile App for Helping People with Disabilities to Search Accessible Shops.

People with motor disabilities must face many barriers and obstacles in their daily lives, making it difficult to perform everyday tasks. The purpose of this work is to improve their living conditions by providing an app with accessibility information in an updated, reliable and friendly form. The development of the system integrates national and regional accessibility regulations, architectural aspects, with an extensive field work, and a sustainable software process. The levels of accessibility and the requirements of the application are defined in the first phases of the project. The field work included the evaluation of 357 commercial establishments in the city of Murcia, Spain, showing that only 25% have a good accessibility, 40% are practicable with help, and 35% are inaccessible shops. The proposed system achieves its objectives of being sustainable and helping in the accessibility. Besides, the system can be a great incentive for businesses to improve their accessibility conditions. In conclusion, new technologies must have a much more active role in the promotion of universal accessibility. These tools must also consider the necessary requirements of sustainable development.

Using ensemble classification methods in lung cancer disease.

This paper presents an overview of the use of ensemble classification methods in the lung cancer disease. An analysis is carried out according to seven aspects: publication trends, channels and venues; medical tasks tackled; ensemble types proposed; single techniques used to construct the ensemble methods; rules used to draw the output of the ensemble; datasets used to build and evaluate the ensemble methods; and tools used. The application of ensemble methods in lung cancer disease started in 2003. The diagnosis task was the most tackled one by researchers. Furthermore, the homogeneous ensembles were the most frequent in the literature, and decision tree techniques were the most adopted ones for constructing ensembles. Several datasets related to the lung cancer disease were used to build and assess the ensemble methods. The most used tool was Weka. To conclude, some recommendations for future research are: tackle the medical tasks not investigated in the literature by means of ensemble methods; investigate other classification methods; propose other heterogeneous ensemble methods; and use other combination rules.

Homogeneous and heterogeneous ensemble classification methods in diabetes disease: a review.

This paper explores the use of ensemble classification methods in the context of the diabetes disease. An analysis was carried out that formulates and answers seven research questions: publication trends, channels and venues; medical tasks undertaken; ensemble types proposed; single techniques used to construct the ensemble methods; rules used to draw the output of the ensemble; datasets used to build and evaluate the ensemble methods; and tools used. A total of 107 papers were chosen after a study selection process. Ensemble methods were applied to diabetes in 2003 for the first time. All medical tasks related to the diabetes disease were investigated, and the diagnosis task was the most frequently addressed activity by means of ensemble methods. The homogeneous ensembles were the most common in the literature. Moreover, decision trees and support vector machines were the most used techniques to build homogeneous and heterogeneous ensembles, respectively. The most frequently found combiner was the majority voting rule. Our findings suggest that ensemble classification methods yield better accuracy than single classifiers. This statement, however, requires an aggregation of the evidence reported in the literature by means of a systematic literature review.

Systematic mapping study of data mining-based empirical studies in cardiology.

Data mining provides the methodology and technology to transform huge amount of data into useful information for decision making. It is a powerful process to extract knowledge and discover new patterns embedded in large data sets. Data mining has been increasingly used in medicine, particularly in cardiology. In fact, data mining applications can greatly benefits all parts involved in cardiology such as patients, cardiologists and nurses. This article aims to perform a systematic mapping study so as to analyze and synthesize empirical studies on the application of data mining techniques in cardiology. A total of 142 articles published between 2000 and 2015 were therefore selected, studied and analyzed according to the four following criteria: year and channel of publication, research type, medical task and empirical type. The results of this mapping study are discussed and a list of recommendations for researchers and cardiologists is provided.

Evaluating the Privacy Policies of Mobile Personal Health Records for Pregnancy Monitoring.

A mobile personal health record (mPHR) for pregnancy monitoring allows the pregnant woman to track and manage her personal health data. However, owing to the privacy and security issues that may threaten the exchange of this sensitive data, a privacy policy should be established. The aim of this study is to evaluate the privacy policies of 19 mPHRs for pregnancy monitoring (12 for iOS and 7 for Android) using a template covering the characteristics of privacy, security, and standards and regulations. The findings of this study show that none of the privacy policies evaluated entirely comply with the characteristics studied. The developers of mPHRs for pregnancy monitoring are, therefore, requested to improve and pay more attention to the structure and the content of the privacy policies of their apps.

Reusable Software Usability Specifications for mHealth Applications.

One of the key factors for the adoption of mobile technologies, and in particular of mobile health applications, is usability. A usable application will be easier to use and understand by users, and will improve user's interaction with it. This paper proposes a software requirements catalog for usable mobile health applications, which can be used for the development of new applications, or the evaluation of existing ones. The catalog is based on the main identified sources in literature on usability and mobile health applications. Our catalog was organized according to the ISO/IEC/IEEE 29148:2011 standard and follows the SIREN methodology to create reusable catalogs. The applicability of the catalog was verified by the creation of an audit method, which was used to perform the evaluation of a real app, S Health, application created by Samsung Electronics Co. The usability requirements catalog, along with the audit method, identified several usability flaws on the evaluated app, which scored 83%. Some flaws were detected in the app related to the navigation pattern. Some more issues related to the startup experience, empty screens or writing style were also found. The way a user navigates through an application improves or deteriorates user's experience with the application. We proposed a reusable usability catalog and an audit method. This proposal was used to evaluate a mobile health application. An audit report was created with the usability issues identified on the evaluated application.

E-health internationalization requirements for audit purposes.

BACKGROUND AND OBJECTIVE: In the 21st century, e-health is proving to be one of the strongest drivers for the global transformation of the health care industry. Health information is currently truly ubiquitous and widespread, but in order to guarantee that everyone can appropriately access and understand this information, regardless of their origin, it is essential to bridge the international gap. The diversity of health information seekers languages and cultures signifies that e-health applications must be adapted to satisfy their needs. METHODS: In order to achieve this objective, current and future e-health programs should take into account the internationalization aspects. This paper presents an internationalization requirements specification in the form of a reusable requirements catalog, obtained from the principal related standards, and describes the key methodological elements needed to perform an e-health software audit by using the internationalization knowledge previously gathered. RESULTS: S Health, a relevant, well-known Android application that has more than 150 million users in over 130 countries, was selected as a target for the e-health internationalization audit method and requirements specification presented above. This application example helped us to put into practice the proposal and show that the procedure is realistic and effective. CONCLUSIONS: The approach presented in this study is subject to continuous improvement through the incorporation of new knowledge originating from additional information sources, such as other standards or stakeholders. The application example is useful for early evaluation and serves to assess the applicability of the internationalization catalog and audit methodology, and to improve them. It would be advisable to develop of an automated tool with which to carry out the audit method.

A systematic review of gamification in e-Health.

Gamification is a relatively new trend that focuses on applying game mechanics to non-game contexts in order to engage audiences and to inject a little fun into mundane activities besides generating motivational and cognitive benefits. While many fields such as Business, Marketing and e-Learning have taken advantage of the potential of gamification, the digital healthcare domain has also started to exploit this emerging trend. This paper aims to summarize the current knowledge regarding gamified e-Health applications. A systematic literature review was therefore conducted to explore the various gamification strategies employed in e-Health and to address the benefits and the pitfalls of this emerging discipline. A total of 46 studies from multiple sources were then considered and thoroughly investigated. The results show that the majority of the papers selected reported gamification and serious gaming in health and wellness contexts related specifically to chronic disease rehabilitation, physical activity and mental health. Although gamification in e-Health has attracted a great deal of attention during the last few years, there is still a dearth of valid empirical evidence in this field. Moreover, most of the e-Health applications and serious games investigated have been proven to yield solely short-term engagement through extrinsic rewards. For gamification to reach its full potential, it is therefore necessary to build e-Health solutions on well-founded theories that exploit the core experience and psychological effects of game mechanics.

Software project management tools in global software development: a systematic mapping study.

Global software development (GSD) which is a growing trend in the software industry is characterized by a highly distributed environment. Performing software project management (SPM) in such conditions implies the need to overcome new limitations resulting from cultural, temporal and geographic separation. The aim of this research is to discover and classify the various tools mentioned in literature that provide GSD project managers with support and to identify in what way they support group interaction. A systematic mapping study has been performed by means of automatic searches in five sources. We have then synthesized the data extracted and presented the results of this study. A total of 102 tools were identified as being used in SPM activities in GSD. We have classified these tools, according to the software life cycle process on which they focus and how they support the 3C collaboration model (communication, coordination and cooperation). The majority of the tools found are standalone tools (77%). A small number of platforms (8%) also offer a set of interacting tools that cover the software development lifecycle. Results also indicate that SPM areas in GSD are not adequately supported by corresponding tools and deserve more attention from tool builders.

Mobile personal health records for pregnancy monitoring functionalities: Analysis and potential.

BACKGROUND AND OBJECTIVE: Personal Health Records (PHRs) are a rapidly growing area of health information technology. PHR users are able to manage their own health data and communicate with doctors in order to improve healthcare quality and efficiency. Mobile PHR (mPHR) applications for mobile devices have obtained an interesting market quota since the appearance of more powerful mobile devices. These devices allow users to gain access to applications that used to be available only for personal computers. This paper analyzes the functionalities of mobile PHRs that are specific to pregnancy monitoring. METHODS: A well-known Systematic Literature Review (SLR) protocol was used in the analysis process. A questionnaire was developed for this task, based on the rigorous study of scientific literature concerning pregnancy and applications available on the market, with 9 data items and 35 quality assessments. The data items contain calendars, pregnancy information, health habits, counters, diaries, mobile features, security, backup, configuration and architectural design. RESULTS: A total of 33 mPHRs for pregnancy monitoring, available for iOS and Android, were selected from Apple App store and Google Play store, respectively. The results show that none of the mPHRs selected met 100% of the functionalities analyzed in this paper. The highest score achieved was 77%, while the lowest was 17%. CONCLUSIONS: In this paper, these features are discussed and possible paths for future development of similar applications are proposed, which may lead to a more efficient use of smartphone capabilities.

The evaluation of i-SIDRA - a tool for intelligent feedback - in a course on the anatomy of the locomotor system.

OBJECTIVE: This paper presents an empirical study of a formative mobile-based assessment approach that can be used to provide students with intelligent diagnostic feedback to test its educational effectiveness. METHOD: An audience response system called SIDRA was integrated with a neural network-based data analysis to generate diagnostic feedback for guided learning. A total of 200 medical students enrolled in a General and Descriptive Anatomy of the Locomotor System course were taught using two different methods. Ninety students in the experimental group used intelligent SIDRA (i-SIDRA), whereas 110 students in the control group received the same training but without employing i-SIDRA. RESULTS: In the students' final exam grades, a statistically significant difference was found between those students that used i-SIDRA as opposed to a traditional teaching methodology (T(162)=2.597; p=0.010). The increase in the number of correct answers during the feedback guided learning process from the first submission to the last submission in four multiple choice question tests was also analyzed. There were average increases of 20.00% (Test1), 11.34% (Test2), 8.88% (Test3) and 13.43% (Test4) in the number of correct answers. In a questionnaire rated on a five-point Likert-type scale, the students expressed satisfaction with the content (M=4.2) and feedback (M=3.5) provided by i-SIDRA and the methodology (M=4.2) used to learn anatomy. CONCLUSIONS: The use of audience response systems enriched with feedback such as i-SIDRA improves medical degree students' performance as regards anatomy of the locomotor system. The knowledge state diagrams representing students' behavior allow instructors to study their progress so as to identify what they still need to learn.

A Computer-Aided Detection System for Digital Chest Radiographs.

Computer-aided detection systems aim at the automatic detection of diseases using different medical imaging modalities. In this paper, a novel approach to detecting normality/pathology in digital chest radiographs is proposed. The problem tackled is complicated since it is not focused on particular diseases but anything that differs from what is considered as normality. First, the areas of interest of the chest are found using template matching on the images. Then, a texture descriptor called local binary patterns (LBP) is computed for those areas. After that, LBP histograms are applied in a classifier algorithm, which produces the final normality/pathology decision. Our experimental results show the feasibility of the proposal, with success rates above 87% in the best cases. Moreover, our technique is able to locate the possible areas of pathology in nonnormal radiographs. Strengths and limitations of the proposed approach are described in the Conclusions.

An Empirical Study of Neural Network-Based Audience Response Technology in a Human Anatomy Course for Pharmacy Students.

This paper presents an empirical study of a formative neural network-based assessment approach by using mobile technology to provide pharmacy students with intelligent diagnostic feedback. An unsupervised learning algorithm was integrated with an audience response system called SIDRA in order to generate states that collect some commonality in responses to questions and add diagnostic feedback for guided learning. A total of 89 pharmacy students enrolled on a Human Anatomy course were taught using two different teaching methods. Forty-four students employed intelligent SIDRA (i-SIDRA), whereas 45 students received the same training but without using i-SIDRA. A statistically significant difference was found between the experimental group (i-SIDRA) and the control group (traditional learning methodology), with T (87) = 6.598, p < 0.001. In four MCQs tests, the difference between the number of correct answers in the first attempt and in the last attempt was also studied. A global effect size of 0.644 was achieved in the meta-analysis carried out. The students expressed satisfaction with the content provided by i-SIDRA and the methodology used during the process of learning anatomy (M = 4.59). The new empirical contribution presented in this paper allows instructors to perform post hoc analyses of each particular student's progress to ensure appropriate training.