Integration of healthcare records into a single application is still a challenging process There are additional issues when data becomes heterogeneous, and its application based on users does not appear to be the same. Hence, we propose an application called MEDSHARE which is a web-based application that integrates the data from various sources and helps the patient to access all their health records in a single point of source. Apart just from the collection of data, this portal enables the process of diagnosis using Natural language processing. The process is carried out by fuzzy logic ruleset which is generated by using NLP packages. The resulted information is given to the SVM classifier which helps in the prediction of diseases resulting in 89% of accuracy and standing the best compared to other classifiers. Finally, the observations resulted are sent to the front end application and the concerned user mobile through text message in their own native language for which translation package is been used.
The worldwide demand for medical care has increased due to the increasing expansion of Covid-19 cases. Therefore, in this case, prompt and precise identification of this illness is crucial. Health professionals are using additional screening techniques including CT imaging as well as chest Xrays for this. Pre-processing the CT scan pictures to eliminate the areas of areas, normalize image contrast, and minimize image noise, however, receives little attention. The seriousness of the Covid- 19 infection must be assessed in addition to the Covid-19 detection and categorization. An ICHOHYBRID model for Covid-19 identification and classification from X-ray, as well as CT scan images, is offered as a solution to these issues. Histogram and morphological image processing methods are used for CT-scan images. The Improved Chicken Swarm Optimization (ICHO) technique is used to find the input image’s histogram threshold. The extracted areas are categorized using the Convolutional Neural Network method based on a feature vector. When infections are found, the CNN algorithm is used to categorize them as severe, moderate, or extremely severe using Support Vector Machine. To eliminate the noise from the test pictures for X-ray imaging, the Adapted Anisotropic Diffusion Filtering (A2DF) approach is used. Once the preprocessing is completed, features are extracted using an Image profile (IP) and Histogram-oriented gradient (HOG) to create a fused HOG and IP feature. Using the HYBRID method, the FHI characteristics are divided into 3 classes. When compared to SVM and CNN, the study provides the best accuracy, with scores of 94.6 for CT scan pictures and 95.6 for X-ray images.
Emotional analysis and data mining has become a hot topic in the field of data mining and natural language analysis as a solidly typed mining activity to analyze the concept of objects (i.e., emotion) expressed in the text. Emotional analysis is an important step in the recommendation process, because it allows you to separate the sense of the root context (e.g., positive or negative). In emotional analysis, the word-of-word (BOW) model is widely used in text classification, similar to how it is used in the modeling of a traditional theme. These two anti-emotional texts are considered very similar to the BOW representation. That is why, as a result of polarity change, machine learning methods often fail. We recommend combining a semantic analysis program with a separator to evaluate work results.
The processing chain of scientific information in particular consists of information series, information storage, data sharing, and records evaluation. In the prevailing device, there is lots of improvisation wished for our health care device. The existing technique our affected person monitoring is a guide and time-consuming process. To conquer the task the proposed work proposed actual data series from sensors, IoT-primarily based totally sharing, and information analytics. This proposed device gives the gain for the respective medical doctor to display the affected person's health 24*7 regardless of geographical location. Example: The medical doctor can display the affected person's health even after the affected person receives discharged. This proposed work implies a health sensor named heartbeat sensor to display affected person fitness. The affected person information is monitoring through the sensorsand transmitted to the Arduino. The actual-time statistics from the COM port have acquired the usage of Net beans and stored in an SQL database. The actual-time information may be monitored with the aid of using each affected person and medical doctor. The real-time statistics are processed from Net beans as datasheet to R programming for statistical evaluation. For Clustering, we use the K-Means algorithm and for Classification, we use the Support Vector Machine. Also relying on the affected person's health situations emergency pills or injections are counseled routinely with the aid of using our device. Also, the affected person statistics have encrypted the usage of an ABE (Attribute-based Encryption) set of rules and saved in the public cloud particularly Dropbox. Thus invoking the statistics evaluation approach facilitates in identifying early stroke in the sufferers and offer medicinal diagnosis immediately.
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