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\"Artificial Intelligence (AI) in general and machine learning (ML) and deep learning (DL) in particular and related digital technologies are a couple of fledging paradigms that the next generation healthcare services are sprouting towards. These digital technologies can transform various aspects of healthcare, leveraging advances in computing and communication power. With a new spectrum of business opportunities, AI-powered healthcare services would improve the lives of patients, their families, and societies. However, the application of AI in the healthcare field requires special attention given the direct implication with human life and well-being. Rapid progress in AI leads to the possibility of exploiting healthcare data for designing practical tools for automated diagnosis of chronic diseases such as dementia and diabetes. This book highlights the current research trends in applying AI models in various disease diagnoses and prognoses to provide enhanced healthcare solutions. The primary audience of the book will be postgraduate students and researchers in the broad domain of healthcare technologies\"-- Provided by publisher.

Alzheimer’s disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) real-world dataset: 294 cognitively normal, 254 stable mild cognitive impairment (MCI), 232 progressive MCI, and 268 AD. It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. In the second layer, the model applies binary classification to detect possible MCI-to-AD progression within three years from a baseline diagnosis. The performance of the model is optimized with key markers selected from a large set of biological and clinical measures. Regarding explainability, we provide, for each layer, global and instance-based explanations of the RF classifier by using the SHapley Additive exPlanations (SHAP) feature attribution framework. In addition, we implement 22 explainers based on decision trees and fuzzy rule-based systems to provide complementary justifications for every RF decision in each layer. Furthermore, these explanations are represented in natural language form to help physicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease risk.

Low birth weight is one of the primary causes of child mortality and several diseases of future life in developing countries, especially in Southern Asia. The main objective of this study is to determine the risk factors of low birth weight and predict low birth weight babies based on machine learning algorithms. Low birth weight data has been taken from the Bangladesh Demographic and Health Survey, 2017-18, which had 2351 respondents. The risk factors associated with low birth weight were investigated using binary logistic regression. Two machine learning-based classifiers (logistic regression and decision tree) were adopted to characterize and predict low birth weight. The model performances were evaluated by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve. The average percentage of low birth weight in Bangladesh was 16.2%. The respondent's region, education, wealth index, height, twin child, and alive child were statistically significant risk factors for low birth weight babies. The logistic regression-based classifier performed 87.6% accuracy and 0.59 area under the curve for holdout (90:10) cross-validation, whereas the decision tree performed 85.4% accuracy and 0.55 area under the curve. Logistic regression-based classifier provided the most accurate classification of low birth weight babies and has the highest accuracy. This study's findings indicate the necessity for an efficient, cost-effective, and integrated complementary approach to reduce and correctly predict low birth weight babies in Bangladesh.

Glioblastoma is an aggressive primary brain tumor that poses many therapeutic difficulties because of the high rate of proliferation, genetic variability, and its immunosuppressive microenvironment. The theory of cancer immunoediting, which includes the phases of elimination, equilibrium, and escape, offers a paradigm for comprehending interactions between the immune system and glioblastoma. Immunoediting indicates the process by which immune cells initially suppress tumor development, but thereafter select for immune-resistant versions leading to tumor escape and progression. The tumor microenvironment (TME) in glioblastoma is particularly immunosuppressive, with regulatory T cells and myeloid-derived suppressor cells being involved in immune escape. To achieve an efficient immunotherapy for glioblastoma, it is crucial to understand these mechanisms within the TME. Existing immunotherapeutic modalities such as chimeric antigen receptor T cells and immune checkpoint inhibitors have been met with some level of resistance because of the heterogeneous nature of the immune response to glioblastoma. Solving these issues is critical to develop novel strategies capable of modulating the TME and re-establishing normal immune monitoring. Further studies should be conducted to identify the molecular and cellular events that underlie the immunosuppressive tumor microenvironment in glioblastoma. Comprehending and modifying the stages of immunoediting in glioblastoma could facilitate the development of more potent and long-lasting therapies.

Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMs.

Nowadays, the socioeconomic status has been changed a lot, so people are now more concerned about their life style and health. They have knowledge about the detrimental effects of synthetic products. That is why they are interested in natural products. Utilization of natural products of plant origin having fewer side effects has gained popularity over the years. There is immense scope for natural products that can intimate health benefits beyond traditional nutrients. Moringa oleifera is one such tree having tremendous nutritional and medicinal benefits. It is rich in macro- and micronutrients and other bioactive compounds which are important for normal functioning of the body and prevention of certain diseases. Leaves, flowers, seeds, and almost all parts of this tree are edible and have immense therapeutic properties including antidiabetic, anticancer, antiulcer, antimicrobial, and antioxidant. Most of the recent studies suggested that Moringa should be used as a functional ingredient in food. The aim of this review is to focus the use of Moringa oleifera as a potential ingredient in food products.

Acoustic actuation techniques offer a promising tool for contactless manipulation of both synthetic and biological micro/nano agents that encompass different length scales. The traditional usage of sound waves has steadily progressed from mid-air manipulation of salt grains to sophisticated techniques that employ nanoparticle flow in microfluidic networks. State-of-the-art in microfabrication and instrumentation have further expanded the outreach of these actuation techniques to autonomous propulsion of micro-agents. In this review article, we provide a universal perspective of the known acoustic micromanipulation technologies in terms of their applications and governing physics. Hereby, we survey these technologies and classify them with regards to passive and active manipulation of agents. These manipulation methods account for both intelligent devices adept at dexterous non-contact handling of micro-agents, and acoustically induced mechanisms for self-propulsion of microrobots. Moreover, owing to the clinical compliance of ultrasound, we provide future considerations of acoustic manipulation techniques to be fruitfully employed in biological applications that range from label-free drug testing to minimally invasive clinical interventions.

In this paper, we investigate the (2+1)-dimensional Kadomtsev-Petviashvili-Benjamin-Bona Mahony equation using two effective methods: the unified scheme and the advanced auxiliary equation scheme, aiming to derive precise wave solutions. These solutions are expressed as combinations of trigonometric, rational, hyperbolic, and exponential functions. Visual representations, including three-dimensional (3D) and two-dimensional (2D) combined charts, are provided for some of these solutions. The influence of the nonlinear parameter p on the wave type is thoroughly examined through diverse figures, illustrating the profound impact of nonlinearity. Additionally, we briefly investigate the Hamiltonian function and the stability of the model using a planar dynamical system approach. This involves examining trajectories, isoclines, and nullclines to illustrate stable solution paths for the wave variables. Numerical results demonstrate that these methods are reliable, straightforward, and potent tools for analyzing various nonlinear evolution equations found in physics, applied mathematics, and engineering.

The doubly dispersive (DD) equation finds extensive utility across scientific and engineering domains. It stands as a significant nonlinear physical model elucidating nonlinear wave propagation within the elastic inhomogeneous Murnaghan’s rod (EIMR). With this in mind, we have focused on the integration of the DD model and the modified Khater (MK) method. Through the wave transformation, this model is effectively converted into an ordinary differential equation. In this paper, the goal of our work is to explore new wave solutions to the DD model by using the MK scheme. These solutions provide extremely helpful insights into the operation of the system. The three-dimensional (3D) plot and two-dimensional (2D) combined plot via the impacts of the parameters are provided for various parameters in this manuscript. We also discussed the dynamical properties of the model, which are accomplished through the bifurcation analysis, and also found the Hamiltonian function. This research makes a substantial contribution to the area by increasing our understanding of wave solutions in the DD, introducing novel investigation tools, and carrying out an in-depth investigation of the bifurcation and stability aspects of the system. As a direct result of this research, novel openings have been uncovered for further investigation and application in the various disciplines of science and engineering.

With the development of Internet of Things (IoT) applications, applying the potential and benefits of IoT technology in the health and environment services is increasing to improve the service quality using sensors and devices. This paper aims to apply GIS-based optimization algorithms for optimizing IoT-based network deployment through the use of wireless sensor networks (WSNs) and smart connected sensors for environmental and health applications. First, the WSN deployment research studies in health and environment applications are reviewed including fire monitoring, precise agriculture, telemonitoring, smart home, and hospital. Second, the WSN deployment process is modeled to optimize two conflict objectives, coverage and lifetime, by applying Minimum Spanning Tree (MST) routing protocol with minimum total network lengths. Third, the performance of the Bees Algorithm (BA) and Particle Swarm Optimization (PSO) algorithms are compared for the evaluation of GIS-based WSN deployment in health and environment applications. The algorithms were compared using convergence rate, constancy repeatability, and modeling complexity criteria. The results showed that the PSO algorithm converged to higher values of objective functions gradually while BA found better fitness values and was faster in the first iterations. The levels of stability and repeatability were high with 0.0150 of standard deviation for PSO and 0.0375 for BA. The PSO also had lower complexity than BA. Therefore, the PSO algorithm obtained better performance for IoT-based sensor network deployment.