IoT Framework for a Decision-Making System of Obesity and Overweight Extrapolation among Children, Youths, and Adults

Approximately 30% of the global population is suffering from obesity and being overweight, which is approximately 2.1 billion people worldwide. The ratio is expected to surpass 40% by 2030 if the current balance continues to grow. The global pandemic due to COVID-19 will also impact the predicted obesity rates. It will cause a significant increase in morbidity and mortality worldwide. Multiple chronic diseases are associated with obesity and several threat elements are associated with obesity. Various challenges are involved in the understanding of risk factors and the ratio of obesity. Therefore, diagnosing obesity in its initial stages might significantly increase the patient’s chances of effective treatment. The Internet of Things (IoT) has attained an evolving stage in the development of the contemporary environment of healthcare thanks to advancements in information and communication technologies. Therefore, in this paper, we thoroughly investigated machine learning techniques for making an IoT-enabled system. In the first phase, the proposed system analyzed the performances of random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), logistic regression (LR), and naïve Bayes (NB) algorithms on the obesity dataset. The second phase, on the other hand, introduced an IoT-based framework that adopts a multi-user request system by uploading the data to the cloud for the early diagnosis of obesity. The IoT framework makes the system available to anyone (and everywhere) for precise obesity categorization. This research will help the reader understand the relationships among risk factors with weight changes and their visualizations. Furthermore, it also focuses on how existing datasets can help one study the obesity nature and which classification and regression models perform well in correspondence to others

Machine-Learning-Enabled Obesity Level Prediction Through Electronic Health Records

Obesity is a critical health condition that severely affects an individual’s quality of life and well-being. The occurrence of obesity is strongly associated with extreme health conditions, such as cardiac diseases, diabetes, hypertension, and some types of cancer. Therefore, it is vital to avoid obesity and or reverse its occurrence. Incorporating healthy food habits and an active lifestyle can help to prevent obesity. In this regard, artificial intelligence (AI) can play an important role in estimating health conditions and detecting obesity and its types. This study aims to see obesity levels in adults by implementing AI-enabled machine learning on a real-life dataset. This dataset is in the form of electronic health records (EHR) containing data on several aspects of daily living, such as dietary habits, physical conditions, and lifestyle variables for various participants with different health conditions (underweight, normal, overweight, and obesity type I, II and III), expressed in terms of a variety of features or parameters, such as physical condition, food intake, lifestyle and mode of transportation. Three classifiers, i.e., eXtreme gradient boosting classifier (XGB), support vector machine (SVM), and artificial neural network (ANN), are implemented to detect the status of several conditions, including obesity types. The findings indicate that the proposed XGB-based system outperforms the existing obesity level estimation methods, achieving overall performance rates of 98.5% and 99.6% in the scenarios explored

Artificial Intelligence and Internet of Things Enabled Intelligent Framework for Active and Healthy Living

Obesity poses several challenges to healthcare and the well-being of individuals. It can be linked to several life-threatening diseases. Surgery is a viable option in some instances to reduce obesity-related risks and enable weight loss. State-of-the-art technologies have the potential for long-term benefits in post-surgery living. In this work, an Internet of Things (IoT) framework is proposed to effectively communicate the daily living data and exercise routine of surgery patients and patients with excessive weight. The proposed IoT framework aims to enable seamless communications from wearable sensors and body networks to the cloud to create an accurate profile of the patients. It also attempts to automate the data analysis and represent the facts about a patient. The IoT framework proposes a co-channel interference avoidance mechanism and the ability to communicate higher activity data with minimal impact on the bandwidth requirements of the system. The proposed IoT framework also benefits from machine learning based activity classification systems, with relatively high accuracy, which allow the communicated data to be translated into meaningful information

Machine Learning and Internet of Things Enabled Monitoring of Post-Surgery Patients: A Pilot Study

Artificial Intelligence (AI) and Internet of Things (IoT) offer immense potential to transform conventional healthcare systems. The IoT and AI enabled smart systems can play a key role in driving the future of smart healthcare. Remote monitoring of critical and non-critical patients is one such field which can leverage the benefits of IoT and machine learning techniques. While some work has been done in developing paradigms to establish effective and reliable communications, there is still great potential to utilize optimized IoT network and machine learning technique to improve the overall performance of the communication systems, thus enabling fool-proof systems. This study develops a novel IoT framework to offer ultra-reliable low latency communications to monitor post-surgery patients. The work considers both critical and non-critical patients and is balanced between these to offer optimal performance for the desired outcomes. In addition, machine learning based regression analysis of patients’ sensory data is performed to obtain highly accurate predictions of the patients’ sensory data (patients’ vitals), which enables highly accurate virtual observers to predict the data in case of communication failures. The performance analysis of the proposed IoT based vital signs monitoring system for the post-surgery patients offers reduced delay and packet loss in comparison to IEEE low latency deterministic networks. The gradient boosting regression analysis also gives a highly accurate prediction for slow as well as rapidly varying sensors for vital sign monitoring

Thyroid disorders, epidemiology and outcome among patients in South Western region: Southern Saudi Arabia

Background: Thyroid gland may have a group of a medical condition that affects its main function. The thyroid gland is located at the front of the neck and produces thyroid hormones. The released hormones go through the blood to many body organs for regulating their function, meaning that it is an endocrine organ. These hormones normally act in the body to regulate energy use, infant development, and childhood development. The study aimed to assess the epidemiology of thyroid disorders among cases in the south-western region, Saudi Arabia, and to assess the reporting quality for these cases data.Methods: A retrospective record based descriptive approach was used through reviewing medical records of all cases that were admitted and diagnosed as thyroid related disorders for different indications in the main hospital (king Khalid Hospital) during the period from January 2018 to January 2020. Data extracted throng pre-structured questionnaire including patient's bio-clinical data, preoperative radiological and laboratory investigations. Also, laryngoscope pre and post operatively was reviewed to record findings.Results: The study included 405 cases with thyroid disorders whose ages ranged from 15 to 71 years old with a mean age of 30.5±10.6 years. Females were 82.7% of the included cases, and 83.8% were Saudi. Thyroid related symptoms were recorded for 1-2 years among 58.1% of the cases and for more than 5 years among 15.8%. Thyroid enlargement was recorded for 73.1% of the cases. The multinodular enlargement was recorded for 53.5% of the cases followed with diffuse thyroid enlargement (27.3%). Regarding the type of surgery undergone, total thyroidectomy was the most recorded followed with lobectomy.Conclusions: The study revealed that the majority of the cases were females at middle age presented with benign lesions with Euthyroid status. The most important conclusion was the significant remarkable underreporting of the different clinical data for the cases with many missing items

Green synthesis of zinc oxide nanoparticles from Wodyetia bifurcata fruit peel extract: multifaceted potential in wound healing, antimicrobial, antioxidant, and anticancer applications

This study focuses on the synthesis, characterization, and use of zinc oxide nanoparticles (ZnONPs) derived from W. bifurcata fruit peel extract. ZnONPs are frequently synthesized utilizing a green technique that is both cost-effective and ecologically friendly. ZnONPs were characterized utilizing analytical techniques. Ultra Violet visible (UV-Vis) spectra showed peaks at 364 nm, confirming the production of ZnONPs. Scanning Electron Microscope analysis indicated that the nanoparticles generated were spherical/agglomerated, with diameters ranging from 11 to 25 nm. FTIR spectroscopy was used to identify the particular functional groups responsible for the nanoparticles’ reduction, stabilization, and capping. Phytochemical analysis of the extract revealed that flavonoids, saponins, steroids, triterpenoids, and resins were present. The antibacterial activity of W. bifurcata synthesised nanoparticles was evaluated against pathogenic bacteria. The ZnONPs antioxidant activity was assessed using DPPH assay. The in vitro cytotoxicity was assessed against prostate cancer PC3 cells. The wound healing potential was assessed by employing in vitro scratch assay and in vivo excision model in Wistar rats. Because of its environmentally benign production, low toxicity, and biocompatibility, ZnONPs exhibited potential antibacterial, antioxidant, anticancer, and wound healing activities, indicating that they could be used in cancer treatment and wound management. Further study is required to examine the fundamental mechanisms and evaluate the safety and effectiveness of the test sample in clinical situations

Enhanced In Vivo Wound Healing Efficacy of a Novel Piperine-Containing Bioactive Hydrogel in Excision Wound Rat Model

These days an extensive amount of the attention of researchers is focused towards exploring bioactive compounds of natural or herbal origin for therapeutic intervention in different ailments of significant importance. One such novel bioactive compound that has a variety of biological properties, including anti-inflammatory and antioxidant activities, is piperine. However, until today, piperine has not been explored for its potential to improve inflammation and enhance healing in acute and chronic wounds. Therefore, the present study aimed to investigate the wound healing potential of piperine hydrogel formulation after topical application. Hydrogels fit the need for a depot system at the wound bed, where they ensure a consistent supply of therapeutic agents enclosed in their cross-linked network matrices. In the present study, piperine-containing carbopol 934 hydrogels mixed with Aloe vera gels of different gel strengths were prepared and characterized for rheological behavior, spreadability, extrudability, and percent (%) content uniformity. Furthermore, the wound healing potential of the developed formulation system was explored utilizing the excision wound healing model. The results of an in vivo study and histopathological examination revealed early and intrinsic healing of wounds with the piperine-containing bioactive hydrogel system compared to the bioactive hydrogel system without piperine. Therefore, the study’s findings establish that the piperine-containing bioactive hydrogel system is a promising therapeutic approach for wound healing application that should be diligently considered for clinical transferability

Body Mass Index and <i>Helicobacter pylori</i> among Obese and Non-Obese Patients in Najran, Saudi Arabia: A Case-Control Study

Objective: We examine obese and non-obese patients with respect to Helicobacter pylori (H. pylori) positive-infection (HPPI) and associated factors, specifically body mass index (BMI). Methods: This study took place in the Department of Endoscopy of a central hospital in the Najran region of Saudi Arabia (SA). A total of 340 obese Saudi patients (BMI &#8805; 30 kg/m2) who had undergone diagnostic upper endoscopy before sleeve gastrectomy, were compared with 340 age and gender-matched control patients (BMI &lt; 30 kg/m2) who had undergone diagnostic upper endoscopy for other reasons. Data collected included diagnosis of HPPI. Descriptive and multivariable binary logistic regression was conducted. Results: Mean patient age was 31.22 &#177; 8.10 years, and 65% were males. The total prevalence of HPPI was 58% (95% CI = 54&#8315;61%) with obese patients presenting significantly more HPPI than non-obese patients (66% vs. 50%, OR = 1.98, 95% CI = 1.45&#8315;2.70, p &lt; 0.0005). Age and gender did not associate significantly with HPPI (p = 0.659, 0.200, respectively) and increases in BMI associated significantly with increases in HPPI (p &lt; 0.0005). BMI remained a significant factor in HPPI when modelled with both age and gender (OR = 1.022, 95% CI = 1.01&#8315;1.03, p &lt; 0.0005). Conclusions: Within the limitations of this study, the significance of HPPI in obese Saudi patients residing in the Najran region in SA was demonstrated alongside the significance role of BMI in HPPI

Physical Activity Monitoring and Classification Using Machine Learning Techniques

Physical activity plays an important role in controlling obesity and maintaining healthy living. It becomes increasingly important during a pandemic due to restrictions on outdoor activities. Tracking physical activities using miniature wearable sensors and state-of-the-art machine learning techniques can encourage healthy living and control obesity. This work focuses on introducing novel techniques to identify and log physical activities using machine learning techniques and wearable sensors. Physical activities performed in daily life are often unstructured and unplanned, and one activity or set of activities (sitting, standing) might be more frequent than others (walking, stairs up, stairs down). None of the existing activities classification systems have explored the impact of such class imbalance on the performance of machine learning classifiers. Therefore, the main aim of the study is to investigate the impact of class imbalance on the performance of machine learning classifiers and also to observe which classifier or set of classifiers is more sensitive to class imbalance than others. The study utilizes motion sensors’ data of 30 participants, recorded while performing a variety of daily life activities. Different training splits are used to introduce class imbalance which reveals the performance of the selected state-of-the-art algorithms with various degrees of imbalance. The findings suggest that the class imbalance plays a significant role in the performance of the system, and the underrepresentation of physical activity during the training stage significantly impacts the performance of machine learning classifiers

Ghrelin gastric tissue expression in patients with morbid obesity and type 2 diabetes submitted to laparoscopic sleeve gastrectomy: immunohistochemical and biochemical study

Introduction. Obesity and type 2 diabetes mellitus (T2DM) are leading causes of morbidity and mortality worldwide. Ghrelin is implicated in the pathophysiology of both disease states. Laparoscopic sleeve gastrectomy is an emerging safe therapeutic technique for patients with morbid obesity. Since the removal of ghrelin-secreting cells by sleeve gastrectomy may be associated with diminished hunger sensation the aim of the study was to: (i) compare body weight and body mass index (BMI) in both obese non-diabetic and obese diabetic patient groups, (ii) determine the ghrelin expression in the resected gastric tissue in both groups, (iii) evaluate relationships between ghrelin cell expression and pre- and post-operative serum ghrelin concentration and glucose levels, and (iv) assess the influence of sleeve gastrectomy on serum glycaemic parameters in this patient population. Material and methods. Twenty morbidly obese female patients from Saudi Arabia, of whom ten suffered from T2DM participated in the study. All subjects underwent laparoscopic sleeve gastrectomy. The removed fundus, body and antrum were biopsied and underwent immunohistochemical staining to detect ghrelin cell expression. Serum samples were assayed for ghrelin concentration and indicators of glycaemic status at the baseline and three months after sleeve gastrectomy. Results. BMI (p &lt; 0.05) and body weight (p &lt; 0.001) were significantly lower in non-diabetic obese patients compared with diabetic patients before and 3 months after the surgery. Also, pre-operative serum ghrelin level was higher in non-diabetic patients compared with diabetic patients group, and postoperative plasma ghrelin level was reduced in diabetic patients (p &lt; 0.001) compared with non-diabetic patients. Gastric fundic mucosa of the diabetic patients exhibited lower number of ghrelin-positive cells (p &lt; 0.05) compared with non-diabetic patients. There were significant negative correlations between pre- and post-operative ghrelin serum level and blood glucose (r = –0.736, p = 0.0002 and r = –0.656, p = 0.0007, respectively) in all patient populations. Conclusions. The results of this study suggest that the diabetic status of obese female patients may affect the incidence of ghrelin cells in three major stomach’s regions and this novel observation warrants further studies