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Background: Breast cancer is the most common malignancy in women worldwide posing greater health challenge. The poor survival rate from breast cancer in India may be attributed to lack of awareness, low rate of screening uptake and presentation of cases in advanced stage. Objectives: To assess the baseline knowledge and effectiveness of educational intervention in improving the knowledge regarding breast cancer. Materials and Methods: A quasi-experimental community-based health educational intervention study was performed among 100 women and 18 female primary healthcare providers residing in urban field practice area. Face-to-face interviews were performed using a questionnaire adapted from the Breast Cancer Awareness Measure (Breast-CAM) Version 2. Data were analyzed using SPSS version 25. To assess the difference between pre- and postintervention scores, Mc-Nemar’s test and paired sample t tests were applied. Association between socio-demographic variables and difference in mean scores was assessed by using ANOVA and independent t test. P value less than 0.05 was taken as statistically significant. Results: The mean pre-intervention score of knowledge items (21 including warning signs, risk factors of breast cancer) among 100 women participants was 7.19 ± 3.45 and postintervention mean score was 18.18 ± 2.42. The mean pre-intervention score among 18 primary healthcare providers was 10.89 ± 4.59 and postintervention score was 19.89 ± 1.28. There was significant improvement in mean scores. The increase in percentage of scores in all 21 items postintervention was significant (Mc-Nemar’s test). There was significant association between socio-demographic profile and increase in mean scores. The study identified various barriers for seeking medical care among women. Conclusion: The community-based educational intervention was effective in enhancing the knowledge regarding breast cancer among women.

Conventional healthcare systems are traditionally challenged by fragmented data, lack of predictive insights, and security concerns, which spouse their effectiveness and efficiency. This paper will cover these gaps by developing an integrated Smart Healthcare System leveraging the power of the Internet of Things and Artificial Intelligence processes. To that end, we have proposed a holistic model that integrates several advanced methodologies to help in enhanced disease prediction and patient monitoring, with data security and privacy protection. We further apply the Hybrid Machine Learning (ML) models specifically; Random Forest Classifier integrated with k-means clustering for the prediction of diseases. This will cluster patients according to their similarity in health characteristics and provide an accurate disease risk prediction with an accuracy of 85-90%. Accordingly, Long Short Term Memory (LSTM) networks will be used for deeper timestamp series analyses with the following input sets: predicted disease probabilities, time-stamped health monitoring data, and patient lifestyle information sets. This model is outstanding both in regard to forecasting disease progression and in detecting anomalous health events with less than a 5% false positive rate. For protection and integrity of the data, we will use an Ethereum blockchain framework with respective smart contracts. The approach will provide secure, immutable health data storage and controlled, traceable access in full compliance with the requirements of various data protection regulations, such as GDPR. What's more, differentially private computations on encrypted data samples are guaranteed by combining homomorphic encryption methods with differential privacy techniques. The former ensures that in any kind of data analysis, at the point of execution, individual patient privacy is maintained, while the latter ensures an accurate, aggregated health data insight for different scenarios. By incorporating these methods, a robust smart healthcare system would be developed, one which, other than the ability to predict and monitor the progression of a disease very precisely, was able to protect patients' data and respect privacy. The same work has far-reaching implications in achieving better patient outcomes through earlier interventions and provision of increased security to the data, apart from enhancing trust in digital solutions for healthcare. [ABSTRACT FROM AUTHOR]

This paper is concerned with the analysis of an extended dissipativity performance for a class of bidirectional associative memory (BAM) neural networks (NNs) having time‐varying delays. To achieve this, the idea of the delay‐partitioning approach is used, where the range of time‐varying delay factors is partitioned into a finite number of equidistant subintervals. A delay‐partitioning based Lyapunov–Krasovskii function is introduced on these intervals, and some new delay‐dependent extended dissipativity results are established in terms of linear matrix inequalities, which also depend on the partition size of the delay factor. Further, numerical examples are performed to acknowledge the extended dissipativity performance of delayed discrete‐time BAM NN; further, four case studies were explored with their simulations to validate the impact of the delay‐partitioning approach. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is the most common malignancy in women worldwide posing greater health challenge. The poor survival rate from breast cancer in India may be attributed to lack of awareness, low rate of screening uptake and presentation of cases in advanced stage. Objectives: To assess the baseline knowledge and effectiveness of educational intervention in improving the knowledge regarding breast cancer. Materials and Methods: A quasi-experimental community-based health educational intervention study was performed among 100 women and 18 female primary healthcare providers residing in urban field practice area. Face-to-face interviews were performed using a questionnaire adapted from the Breast Cancer Awareness Measure (Breast-CAM) Version 2. Data were analyzed using SPSS version 25. To assess the difference between pre- and postintervention scores, Mc-Nemar's test and paired sample t tests were applied. Association between socio-demographic variables and difference in mean scores was assessed by using ANOVA and independent t test. P value less than 0.05 was taken as statistically significant. Results: The mean pre-intervention score of knowledge items (21 including warning signs, risk factors of breast cancer) among 100 women participants was 7.19 ± 3.45 and postintervention mean score was 18.18 ± 2.42. The mean pre-intervention score among 18 primary healthcare providers was 10.89 ± 4.59 and postintervention score was 19.89 ± 1.28. There was significant improvement in mean scores. The increase in percentage of scores in all 21 items postintervention was significant (Mc-Nemar's test). There was significant association between socio-demographic profile and increase in mean scores. The study identified various barriers for seeking medical care among women. Conclusion: The community-based educational intervention was effective in enhancing the knowledge regarding breast cancer among women. [ABSTRACT FROM AUTHOR]

[Display omitted] The most frequent cause of blindness in industrialized nations is age-related macular degeneration (AMD), particularly in persons over 60. In recent years in this field, three key factors have led to an increase in the workload of experts and the healthcare system: 1) population aging worldwide prevalence, 2) AMD's chronic nature, and 3) more widespread usage of the retinal optical coherence tomography (OCT) imaging technology. The establishment of fully automated diagnosis systems has recently been made possible by recent developments in the deep learning field. The main objective of the research is to develop a new deep learning-based model using OCT images. The performance for classification and segmentation of retinal diseases is improved by the proposed model through effectively capturing inter-scale features and combining these features using convolutional blocks. Then, we classify volumetric OCT images via a new deep learning-based model. For multi-class classification and segmentation of ocular diseases, the OCT scans of the human eye are used in this research to demonstrate a Deep Learning Network (DL-Net) model by automatically recognizing normal, diabetic macular edema (DME), choroidal neovascularization (CNV), AMD, and drusen images. This research introduces the Modified ResNet-50 approach and Image Processing for multiple OCT image classification. This research presents an efficient diagnostic technique for image segmentation based on a Bi-LSTM-based deep recurrent convolutional neural network (DRCNN). The modified SqueezeNet model analyzes the volumetric segmentation of OCT images. The volumetric publicly available datasets are used to analyze the proposed model. The proposed multi-scale structure model outperforms several well-known OCT classification and segmentation frameworks. 99.76% accuracy is achieved by the proposed model respectively. In healthcare settings, the proposed methodology can be used as a diagnostic instrument to help medical experts make better-diagnosing decisions, as shown by the encouraging proposed architecture's qualitative evaluations and quantitative results by generating a confusion matrix. [ABSTRACT FROM AUTHOR]