6 research outputs found
Blockchain associated machine learning and IoT based hypoglycemia detection system with auto-injection feature
Hypoglycemia is an unpleasant phenomenon caused by low blood glucose. The
disease can lead a person to death or a high level of body damage. To avoid
significant damage, patients need sugar. The research aims at implementing an
automatic system to detect hypoglycemia and perform automatic sugar injections
to save a life. Receiving the benefits of the internet of things (IoT), the
sensor data was transferred using the hypertext transfer protocol (HTTP)
protocol. To ensure the safety of health-related data, blockchain technology
was utilized. The glucose sensor and smartwatch data were processed via Fog and
sent to the cloud. A Random Forest algorithm was proposed and utilized to
decide hypoglycemic events. When the hypoglycemic event was detected, the
system sent a notification to the mobile application and auto-injection device
to push the condensed sugar into the victims body. XGBoost, k-nearest neighbors
(KNN), support vector machine (SVM), and decision tree were implemented to
compare the proposed models performance. The random forest performed 0.942
testing accuracy, better than other models in detecting hypoglycemic events.
The systems performance was measured in several conditions, and satisfactory
results were achieved. The system can benefit hypoglycemia patients to survive
this disease
Internet of Things (IoT) based ECG System for Rural Health Care
Nearly 30% of the people in the rural areas of Bangladesh are below the
poverty level. Moreover, due to the unavailability of modernized
healthcare-related technology, nursing and diagnosis facilities are limited for
rural people. Therefore, rural people are deprived of proper healthcare. In
this perspective, modern technology can be facilitated to mitigate their health
problems. ECG sensing tools are interfaced with the human chest, and requisite
cardiovascular data is collected through an IoT device. These data are stored
in the cloud incorporates with the MQTT and HTTP servers. An innovative
IoT-based method for ECG monitoring systems on cardiovascular or heart patients
has been suggested in this study. The ECG signal parameters P, Q, R, S, T are
collected, pre-processed, and predicted to monitor the cardiovascular
conditions for further health management. The machine learning algorithm is
used to determine the significance of ECG signal parameters and error rate. The
logistic regression model fitted the better agreements between the train and
test data. The prediction has been performed to determine the variation of
PQRST quality and its suitability in the ECG Monitoring System. Considering the
values of quality parameters, satisfactory results are obtained. The proposed
IoT-based ECG system reduces the health care cost and complexity of
cardiovascular diseases in the future
A deep learning approach for brain tumor detection using magnetic resonance imaging
The growth of abnormal cells in the brain’s tissue causes brain tumors. Brain tumors are considered one of the most dangerous disorders in children and adults. It develops quickly, and the patient’s survival prospects are slim if not appropriately treated. Proper treatment planning and precise diagnoses are essential to improving a patient’s life expectancy. Brain tumors are mainly diagnosed using magnetic resonance imaging (MRI). As part of a convolution neural network (CNN)-based illustration, an architecture containing five convolution layers, five max-pooling layers, a Flatten layer, and two dense layers has been proposed for detecting brain tumors from MRI images. The proposed model includes an automatic feature extractor, modified hidden layer architecture, and activation function. Several test cases were performed, and the proposed model achieved 98.6% accuracy and 97.8% precision score with a low cross-entropy rate. Compared with other approaches such as adjacent feature propagation network (AFPNet), mask region-based CNN (mask RCNN), YOLOv5, and Fourier CNN (FCNN), the proposed model has performed better in detecting brain tumors