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A stroke is a serious, life-threatening condition that occurs when the blood supply to a part of the brain is cut off. The earlier a stroke is treated, the less damage is likely to occur. One of the methods that can lead to faster treatment is timely and accurate prediction and diagnosis. This paper aims to compare the binary integer programming-data envelopment analysis (BIP-DEA) model and the logistic regression (LR) model for diagnosing and predicting the occurrence of stroke in Iran.

In this study, two algorithms of the BIP-DEA and LR methods were introduced and key risk factors leading to stroke were extracted.

The study population consisted of 2,100 samples (patients) divided into six subsamples of different sizes. The classification table of each algorithm showed that the BIP-DEA model had more reliable results than the LR for the small data size. After running each algorithm, the BIP-DEA and LR algorithms identified eight and five factors as more effective risk factors and causes of stroke, respectively. Finally, predictive models using the important risk factors were proposed.

The main objective of this study is to provide the integrated BIP-DEA algorithm as a fast, easy and suitable tool for evaluation and prediction. In fact, the BIP-DEA algorithm can be used as an alternative tool to the LR model when the sample size is small. These algorithms can be used in various fields, including the health-care industry, to predict and prevent various diseases before the patient’s condition becomes more dangerous.

This study aims to perform three-dimensional numerical computations for blood flow through a double stenosed carotid artery. Pulsatile flow with Womersley number (Wo) of 4.65 and Reynolds number (Re) of 425, based on the diameter of normal artery and average velocity of inlet pulse, was considered.

Finite volume method based ANSYS Fluent 20.1 was used for solving the governing equations of three-dimensional, laminar, incompressible and non-Newtonian blood flow. A high-quality grid with sufficient refinement was generated using ICEM CFD 20.1. The time-averaged flow field was captured to investigate the effect of severity and eccentricity on the lumen flow characteristics.

The results show that an increase in interspacing between blockages brings shear layer instability within the region between two blockages. The velocity profile and wall shear stress distribution are found to be majorly influenced by eccentricity. On the other hand, their peak magnitude is found to be primarily influenced by severity. Results have also demonstrated that the presence of eccentricity in stenosis would assist in flow development.

Variation in severity and interspacing was considered with a provision of eccentricity equal to 10% of diameter. Eccentricity refers to the offset between the centreline of stenosis and the centreline of normal artery. For the two blockages, severity values of 40% and 60% based on diameter reduction were permuted, giving rise to four combinations. For each combination, three values of interspacing in the multiples of normal artery diameter (D), viz. 4D, 6D and 8D were considered.

The purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and observe the important gaps in the literature that can inform a research agenda going forward.

A systematic literature strategy was utilized to identify and analyze scientific papers between 2012 and 2022. A total of 28 articles were identified and reviewed.

The outcomes reveal that while advances in machine learning have the potential to improve service access and delivery, there have been sporadic growth of literature in this area which is perhaps surprising given the immense potential of machine learning within the health sector. The findings further reveal that themes such as recordkeeping, drugs development and streamlining of treatment have primarily been focused on by the majority of authors in this area.

The search was limited to journal articles published in English, resulting in the exclusion of studies disseminated through alternative channels, such as conferences, and those published in languages other than English. Considering that scholars in developing nations may encounter less difficulty in disseminating their work through alternative channels and that numerous emerging nations employ languages other than English, it is plausible that certain research has been overlooked in the present investigation.

This review provides insights into future research avenues for theory, content and context on adoption of machine learning within the health sector.

Existing research of modern literature have shown that the phenomenon of digital entrepreneurship is lacking in robust theoretical foundations on several occasions. This article is a comprehensive literature study that focuses on the phenomena of digital entrepreneurship and offers views on the subject to provide insights into recent advancements in the area.

In order to achieve a conception of the phenomena, using the PRISMA flow chart, the significant findings were organised into themes, contexts and approaches. A comprehensive evaluation of the relevant previous research was carried out. Both the Web of Science and Scopus were utilised to locate, extract, select and evaluate relevant papers based on the keywords found during the search. In the end, papers from 92 different publications that are indexed by SSCI were chosen for this investigation.

This comprehensive literature analysis was to identify current research routes on digital entrepreneurship. In conclusion, this study generates outcomes that describe the process by which digital entrepreneurship are recognised and discussed: digital business models; digital entrepreneurship process; platform tactics; technology adoption; entrepreneurship and digital business.

By setting the framework for additional research development and motivating scholars to pursue this issue, the study contributes to the understanding of the conceptualisation of digital entrepreneurship.

In the state of Hawaii, it has been shown that certain ethnic minority groups, such as Filipinos and Pacific Islanders, suffer disproportionally high rates of cardiovascular disease, evidence that local health-care systems and governing bodies fail to equally extend the human right to health to all. This study aims to examine whether these ethnic health disparities in cardiovascular disease persist even within an already globally disadvantaged group, the houseless population of Hawaii.

A retrospective chart review of records from Hawaii Houseless Outreach and Medical Education Project clinic sites from 2016 to 2020 was performed to gather patient demographics and reported histories of type II diabetes, obesity, hyperlipidemia, hypertension and other cardiovascular disease diagnoses. Reported disease prevalence rates were compared between larger ethnic categories as well as ethnic subgroups.

Unexpectedly, the data revealed lower reported prevalence rates of most cardiometabolic diseases among the houseless compared to the general population. However, multiple ethnic health disparities were identified, including higher rates of diabetes and obesity among Native Hawaiians and other Pacific Islanders and higher rates of hypertension among Filipinos and Asians overall. The findings suggest that even within a generally disadvantaged houseless population, disparities in health outcomes persist between ethnic groups and that ethnocultural considerations are just as important in caring for this vulnerable population.

To the best of the authors’ knowledge, this is the first comprehensive study focusing on ethnic health disparities in cardiovascular disease and the structural processes that contribute to them, among a houseless population in the ethnically diverse state of Hawaii.