Healthcare

Falls are a serious concern among the elderly due to being a major cause of harm to their physical and mental health. Despite their potential for harm, they can be prevented with proper care and monitoring. As such, the motivation for this research is to implement an algorithmic solution to the problem of falls that leverages the benefits of Machine Learning to detect falls in the elderly. There are various studies on fall detection that works on one dataset: wearable, environmental, or vision. Such an approach is biased against low fall detection and has a high false alarm rate. According to the literature, using two datasets can result in high accuracy and lower false alarms. The purpose of this study is to contribute to the field of Machine Learning and Fall Detection by investigating the optimal ways to apply common machine and deep learning algorithms trained on multimodal fall data. In addition, it has proposed a multimodal approach by training two separate classifiers using both Machine and Deep Learning and combining them into an overall system using sensor fusion in the form of a majority voting approach. Each trained model outputs an array comprising three percentage numbers, the average of the numbers in the same class from both arrays is then computed, and the highest percentage is the classification result. The working system achieved results were 97% accurate, with the highest being achieved by the Convolutional Neural Network algorithm. These results were higher than other state-of-the-art research conducted in the field.

Heart failure is a severe medical ailment that significantly impacts patients’ well-being and the healthcare system. For improved results, early detection and immediate treatment are essential. This work aims to develop and evaluate predictive models by applying sophisticated ensemble learning techniques. In order to forecast heart failure, we used a clinical dataset from Kaggle. We used the well-known ensemble techniques of bagging and random forest (RF) to create our models. With a predicted accuracy of 82.74%, the RF technique, renowned for its versatility and capacity to handle complex data linkages, fared well. The bagging technique, which employs several models and bootstrapped samples, also demonstrated a noteworthy accuracy of 83.98%. The proposed model achieved an accuracy of 90.54%. These results emphasize the value of group learning in predicting cardiac failure. The area under the ROC curve (AUC) was another metric to assess the model’s discriminative ability, and our model achieved 94% AUC. This study dramatically improves the prognostic modeling for heart failure. The findings have extensive implications for clinical practice and healthcare systems and offer a valuable tool for early detection and intervention in cases of heart failure.

 Objective: This research demonstrates that the practice of mindfulness training led to a notable decrease in occupational stress and an improvement in the overall quality of life for professionals working in operating rooms.Methods: The method of the present research was practical in nature and quasi-experimental in the form of pretest-posttest with a control group. The statistical population of this research included all operating room experts in Tehran hospitals. 50 people from this community were selected by convenience sampling method and were randomly divided into two experimental (25 people) and control (25 people) groups. Data was collected using standard questionnaires. T tests and ANCOVA were used to analyze data. Results: The results showed that there is no significant difference in both occupational stress and quality of life in the pretest (both P>0.05). However, it was observed that experimental group had significantly lower occupational stress and higher quality of life compared to control group in the posttest (both P=0.001). Finally, the results of ANCOVA showed significant differences between experimental and control groups in both occupational stress and quality of life (both P=0.001).Conclusion: These findings indicate that mindfulness can play a crucial role in managing work-related stress among operating room experts. Consequently, by actively participating in mindfulness activities, it is plausible to create more favorable circumstances for professionals working in operating rooms in terms of their quality of life.

This study aims to assess the efficiency of the eHealth program by conducting a comparative study between public and private healthcare settings in Bangladesh and Malaysia. A detailed literature review gathered from leading databases, articles, and case studies was conducted to achieve the study’s objective. After evaluating the literature and utilizing reducing methods the factors were organized and categorized into the four SWOT groups. The study exhibits that the government of Bangladesh has taken multiple approaches to efficient usage of their resources and providing primary healthcare services at grassroots levels. However, compared to a developing country like Malaysia it is far lag and struggling with multiple challenges like inadequate ICT infrastructure interoperability issues, low adoption of services, privacy and trust issues inadequate data security lack of policies and regulations, and insufficient funding along with multiple threats like resistance to change, cultural influence, and native environmental issues. Nonetheless, these challenges can be addressed through the development of adequate infrastructure, technology, financial support, and human resource capabilities. This will help to achieve the goal of universal health coverage and digital healthcare. This paper identified and explored numerous eHealth concerns and institutionalized favorable policy recommendations to establish an effective and successful eHealth system and boost the healthcare sectors of Bangladesh and similar emerging economies.  Being the first to incorporate SWOT analysis and eHealthcare programs, the suggested approaches can have a significant positive impact on the implementation of an effective and sustainable eHealth system.