Background: Stunting is a major public health issue in most of developing countries. Although, its worldwide prevalence is decreasing slowly but the number of stunted children is still rising in Pakistan. Stunting is highly associated with several long-term consequences, including higher rate of mortality and morbidity, deficient cognitive growth, school performance, learning capacity, work capacity and work productivity. To prevent stunting, we proposed Stunting Diagnostic and Education app. This app includes detailed knowledge of stunting and it's all forms, symptoms, causes, video tutorials and guidelines by the Pediatricians and Nutritionists. Methods: A cross-sectional study has been conducted in schools of Multan District, Pakistan for the period of January 2019 to June 2019. Sample data of 1420 children, aged 4 to 18 years using three age groups, were analyzed by using SPSS version 21.0 to assess the prevalence of stunting and to analyze the risk factors associated with it in children under and over 5 age. Chi square test was applied in comparison with rural and urban participants and p-value < 0.05 was considered as significant. This study includes distribution of sociodemographic characteristics, parental education, working status of mothers, dietary patterns of school going children and prevalence of stunting in school going children. After getting study results, Stunting Diagnostic and Education app was developed according to the instructions of child experts and nutritionists. Results: 354 (24.93%) participants were stunted out of 1420, 11.9% children were obese and 63.17% children were normal. Out of 354 stunted children, higher ratio of stunting was found in the age group of 8-11 years children with 51.98 percentage. 37.85% stunted children were found in the age group of 4-7 years and 10.17% stunting was found in the age group of 12-18 years children. It was observed in the study that male children were highly stunted than female with 57.91 and 42.09% respectively. Children living in rural areas were more stunted affected as compared to the children living in urban society with percentage 58.76 and 41.24 respectively.
Background Chronic Kidney Disease (CKD), i.e., gradual decrease in the renal function spanning over a duration of several months to years without any major symptoms, is a life-threatening disease. It progresses in six stages according to the severity level. It is categorized into various stages based on the Glomerular Filtration Rate (GFR), which in turn utilizes several attributes, like age, sex, race and Serum Creatinine. Among multiple available models for estimating GFR value, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), which is a linear model, has been found to be quite efficient because it allows detecting all CKD stages. Methods Early detection and cure of CKD is extremely desirable as it can lead to the prevention of unwanted consequences. Machine learning methods are being extensively advocated for early detection of symptoms and diagnosis of several diseases recently. With the same motivation, the aim of this study is to predict the various stages of CKD using machine learning classification algorithms on the dataset obtained from the medical records of affected people. Specifically, we have used the Random Forest and J48 algorithms to obtain a sustainable and practicable model to detect various stages of CKD with comprehensive medical accuracy. Results Comparative analysis of the results revealed that J48 predicted CKD in all stages better than random forest with an accuracy of 85.5%. The study also showed that J48 shows improved performance over Random Forest. Conclusions The study concluded that it may be used to build an automated system for the detection of severity of CKD.
Background Infectious diseases often lead to death among children under 5 years in many underdeveloped and developing countries. One of the main reasons behind this is an unawareness of disease symptoms among mothers and child caregivers. To overcome this, we propose the EasyDetectDisease mobile health app to educate mothers about the early symptoms of pediatric diseases and to provide them with practical advice for preventing the spread of such diseases in children under 5 years. The EasyDetectDisease app includes detailed knowledge of infectious diseases, including the corresponding symptoms, causes, incubation period, preventive measures, nutritional guidelines such as breastfeeding, video tutorials of child patients, and video guidelines by pediatric health experts to promote child health. It also provides information on the diagnosis of the infectious diseases based on symptoms. Objective The objective of this study was to evaluate the usability (eg, ease of use, easy detection of disease, functionality, and navigation of interfaces) of the EasyDetectDisease app among mothers of children under 5 years of age. Methods Two health sessions, held in Pakistan, were used to evaluate the usability of EasyDetectDisease by 30 mothers of children under 5 years. The app was evaluated based on various quantitative and qualitative measures. Results The participating mothers confirmed that they were able to diagnose diseases accurately and that after following the instructions provided, their children recovered rapidly without any nutritional deficiency. All participating mothers showed an interest in using the EasyDetectDisease app if made available by governmental public health agencies, and they suggested its inclusion in all mobile phones as a built-in health app in the future. Conclusions EasyDetectDisease was modified into a user-friendly app based on feedback collected during the usability sessions. All participants found it acceptable and easy to use, especially illiterate mothers. The EasyDetectDisease app proved to be a useful tool for child health care at home and for the treatment of infectious diseases and is expected to reduce the mortality rate of children under 5 years of age.
Chronic Kidney Disease (CKD), i.e., gradual decrease in the renal function spanning over a duration of several months to years without any major symptoms, is a life-threatening disease. It progresses in six stages according to the severity level. It is categorized into various stages based on the Glomerular Filtration Rate (GFR), which in turn utilizes several attributes, like age, sex, race and Serum Creatinine. Among multiple available models for estimating GFR value, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), which is a linear model, has been found to be quite efficient because it allows detecting all CKD stages i.e., early stage to the last stage of kidney failure. Early detection and cure of CKD is extremely desirable as it can lead to the prevention of unwanted consequences. Machine learning are being extensively advocated for early detection of symptoms and diagnosis of several diseases recently. With the same motivation, the aim of this study is to predict the various stages of CKD using machine learning classification algorithms on the dataset obtained from the medical records of affected people. In particular, we have used the Random Forest and J48 algorithms to obtain a sustainable and practicable model to detect various stages of CKD with comprehensive medical accuracy. Comparative analysis of the results revealed that J48 predicted CKD in all stages better than random forest with a 85.5% accuracy. The study also showed that J48 shows improved performance over Random Forest, so, it may be used to build an automated system for the detection of severity of CKD.
With the advent of computer networks, e-mentoring becomes feasible and indispensable to enlighten protégés. E-mentoring comes into play where conventional mentoring is unable to assist students, if they are unable to reach at specified location and time. This chapter elucidates concepts, challenges, impact, and evaluation of e-mentoring by referencing scholars. This chapter retains juxtaposition of traditional mentoring and e-mentoring, which is computer-mediated communication (CMC). It also explains technologies for e-mentoring like web-based and different programs that have been carried out in literature. This chapter also includes best practices and the role of e-mentoring in different fields such as medicines, entrepreneurs, and for students with disabilities.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.