Blood Glucose Regulation for Post-Operative Patients with Diabetics and Hypertension Continuum: A Cascade Control-Based Approach

Fact Finding Instructor-based Clustering Technique for BP Estimation using Human Speech Signals

Blood Pressure (BP) is considered an essential factor that provides information regarding cardiovascular function. Regular monitoring of the BP is required for proper healthcare maintenance that avoids the high risk of life due to high and low BP. Several methods were devised for the estimation of BP, but the estimation accuracy is still a challenging task. Hence this research introduces an efficient BP estimation technique using the Fact Finding Instructor (FFI) based clustering method by considering the speech signal of the patients. An efficient BP extraction technique is introduced using the FFI Optimization algorithm an integration of the mannerism of the fact finder that identifies the suspect who commits the criminal offense and, with the instructor with good knowledge, these make the trainee more efficient. The detection and suspect's arrest contain two phases, the fact-finding phase and the chasing phase. Initially, the speech signal is collected from the database and pre-processed for removing noise and artifacts. Then feature extraction is used for the minimization of the computation overhead that generates a feature vector. The clustering of BP is employed with the k-means clustering algorithm and the proposed FFI optimization algorithm. The FFI Optimization algorithm provides a fast convergence rate due to the fact-finding phase and provides accurate detection of the suspect's location along with that the clustering of classes of patients' BP by considering the feature of the speech signal. The clusters formed using the FFI optimization algorithm are combined with the K-means clustering, by multiplying the clusters the BP estimation is implemented on three criteria Low BP, Normal, and, High BP. Finally, the output generated by both the clustering operations is multiplied together for the estimation of the BP. The performance of the proposed method is evaluated using the metrics like Davies Bouldin score, Homogeneity score, Completeness score, Jacquard Similarity score, Silhouette score, and Dunn's Index which acquired the improvement rate of 0.98, 0.96, 0.96, 0.98, 0.95, and 0.98 for training percentage 90, respectively to the existing Teaching Learning Based Optimization(TLBO) clustering technique.

Accurate computation: COVID-19 rRT-PCR positive test dataset using stages classification through textual big data mining with machine learning

In every field of life, advanced technology has become a rapid outcome, particularly in the medical field. The recent epidemic of the coronavirus disease 2019 (COVID-19) has promptly become outbreaks to identify early action from suspected cases at the primary stage over the risk prediction. It is overbearing to progress a control system that will locate the coronavirus. At present, the confirmation of COVID-19 infection by the ideal standard test of reverse transcription-polymerase chain reaction (rRT-PCR) by the extension of RNA viral, although it presents identified from deficiencies of long reversal time to generate results in 2-4 h of corona with a necessity of certified laboratories. In this proposed system, a machine learning (ML) algorithm is used to classify the textual clinical report into four classes by using the textual data mining method. The algorithm of the ensemble ML classifier has performed feature extraction using the advanced techniques of term frequency-inverse document frequency (TF/IDF) which is an effective information retrieval technique from the corona dataset. Humans get infected by coronaviruses in three ways: first, mild respiratory disease which is globally pandemic, and human coronaviruses are caused by HCoV-NL63, HCoV-OC43, HCoV-HKU1, and HCoV-229E; second, the zoonotic Middle East respiratory syndrome coronavirus (MERS-CoV); and finally, higher case casualty rate defined as severe acute respiratory syndrome coronavirus (SARS-CoV). By using the machine learning techniques, the three-way COVID-19 stages are classified by the extraction of the feature using the data retrieval process. The TF/IDF is used to measure and evaluate statistically the text data mining of COVID-19 patient's record list for classification and prediction of the coronavirus. This study established the feasibility of techniques to analyze blood tests and machine learning as an alternative to rRT-PCR for detecting the category of COVID-19-positive patients.

An Enhanced Symptom Clustering with Profile Based Prescription Suggestion in Biomedical application

The application of data mining has been increasing day to day whereas the data base is also enhancing simultaneously. Hence retrieving required content from a huge data base is a critical task. This paper focus on biomedical engineering field, it concentrates on initial stage of database such as data preprocessing and cleansing to deal with noise and missing data in large biomedical data sets. The database of biomedical is huge and enhancing nature retrieving of specific content will be a critical task. Suggesting prescription with respect to identified disease based on profile analysis of specific patient is not available in current system. This paper proposes a recommendation system of prescription based on disease identification is done by combining user and professional suggestion with profile based analysis. Hence this focuses on profile based suggestions and report will be generated. The retrieving of specific suggestion from a huge database is done by hybrid feature selection algorithm. This approach focuses on enabling recommendation based on user profile and implementing Hybrid feature selection algorithm to retrieve specific content from a huge database. Hence it attains better retrieval of required content from a huge database compared to other existing approaches and suggests better recommendation with respect to user profile.