An Implementation of Cardiovascular Disease Prediction in Ultrasonography Images using AWMYOLOv4 Deep Learning Mode

Cardiovascular diseases are one of the most important issues facing the people and their origins also death is contained all over the world the facing issues in past 25 years. Every country’s inversing large amount in health care researches and it’s related to enhanced predict the diseases. Cardio issues are not even physicians can easily be predicted and it is a very challenging task that requires high knowledge and expertise. To identify to create machine language models used to efficiently predict the earliest stage of cardiovascular disease. In this work, we recommend AWMF filter for the pre-process the Input Image after the input move to YOLOv4 neural network method for classification and segmentation to the heart affected areas by using ultrasonic Images with the help of a machine learning algorithm. The proposed algorithm uses ultrasonic picture classification and segmentation to detect cardiovascular disease earlier. This model shows the more accurate result on 96% of training and 98% testing data. And this method shows better results and providing while compared to the existing method

SCREENING AND OPTIMIZATION OF VALACYCLOVIR NIOSOMES BY DESIGN OF EXPERIMENTS

Objective: The objective of the study was to perform a screening, optimization of valacyclovir niosomal formulation to achieve a sustained release of drug using the design of experiments by 32 full factorial design.Methods: Valacyclovir loaded niosomes were prepared using thin film hydration method by varying the ratio of Span 60 and Cholesterol. The prepared niosomes were evaluated for vesicle size, entrapment efficiency, cumulative drug release, fourier transformed infrared spectroscopy (FTIR), zeta potential and surface morphology by field emission scanning electron microscopy (FESEM).Results: The valacyclovir was successfully encapsulated and its entrapment efficiency ranged from 36.70 % to 50.62 %. The average vesicle size of the niosomes was found to be 431 to 623 nm. At 8th hour the drug release varied from 77.50% to 96.31 %. The optimized niosomes were multilamellar with a surface charge potential of about-43.2 mV. The studies revealed that the interaction of cholesterol and surfactant had a substantial effect on vesicle size, entrapment efficiency and drug release from the niosomes. The release kinetics of the optimized niosomes followed zero order kinetics with fickian diffusion controlled mechanism. The stability studies were performed for the optimized formulation and found that the formulation is stable at 4°C ± 2°C.Conclusion: Model equations were developed for the responses. No significant difference was observed between the predicted and observed value, showing that the developed model is reliable

CFLCA: High Performance based Heart disease Prediction System using Fuzzy Learning with Neural Networks

Human Diseases are increasing rapidly in today’s generation mainly due to the life style of people like poor diet, lack of exercises, drugs and alcohol consumption etc. But the most spreading disease that is commonly around 80% of people death direct and indirectly heart disease basis. In future (approximately after 10 years) maximum number of people may expire cause of heart diseases. Due to these reasons, many of researchers providing enormous remedy, data analysis in various proposed technologies for diagnosing heart diseases with plenty of medical data which is related to heart disease. In field of Medicine regularly receives very wide range of medical data in the form of text, image, audio, video, signal pockets, etc. This database contains raw dataset which consist of inconsistent and redundant data. The health care system is no doubt very rich in aspect of storing data but at the same time very poor in fetching knowledge. Data mining (DM) methods can help in extracting a valuable knowledge by applying DM terminologies like clustering, regression, segmentation, classification etc. After the collection of data when the dataset becomes larger and more complex than data mining algorithms and clustering algorithms (D-Tree, Neural Networks, K-means, etc.) are used. To get accuracy and precision values improved with proposed method of Cognitive Fuzzy Learning based Clustering Algorithm (CFLCA) method. CFLCA methodology creates advanced meta indexing for n-dimensional unstructured data. The heart disease dataset used after data enrichment and feature engineering with UCI machine learning algorithm, attain high level accurate and prediction rate. Through this proposed CFLCA algorithm is having high accuracy, precision and recall values of data analysis for heart diseases detection

Kushner-Stratonovich Dice Segmented Curvelet (KSDSC) deep convolutional neural learning for heart disease prediction

Data mining is commonly used method for processing large amount of data in heart disease prediction. Many heart disease prediction researches are carried out by different authors. But the accuracy level was not improved. In order to address these issues, Kushner-Stratonovich Dice Segmented Haar Wavelet (KSDSC) Deep Convolutional Neural Learning Model is introduced. The main aim of KSDSC Model is to perform efficient heart disease prediction with five layers, namely one input layer, three hidden layers and one output layer. Initially, ultrasound images are collected as an input at input layer. An image pre-processing is carried out using Kushner-Stratonovich Filter to eliminate the noisy pixels from US image and transmitted to hidden layer 2. Sørensen–Dice Image Segmentation Process partitions the preprocessed image into number of divisions in hidden layer 2. After that in hidden layer 3, the multiple features are extracted using Curvelet transform from segmented image and sent to the output layer. The output layer uses softmax yolov4 darknet53 activation function to match extracted features for heart disease prediction. Experimental analysis is performed on parameters such as prediction accuracy, false positive rate, and prediction time with respect to a number of US images

Impact of clinical pharmacist's educational intervention tools in enhancing public awareness and perception of antibiotic use: A randomized control trial

Introduction: In most developing countries, antimicrobial resistance is a public threat, and insufficient knowledge of antibiotics among the public adds to it. This study aimed to determine the efficacy of the clinical pharmacist's pamphlets and video-based educational tools to address the public's knowledge, attitude, and practice gaps on antibiotic use. Methods: This was a pre-and post-intervention cohort study of the adult population in South India who can read and understand Tamil or English. The study participants were designated into two groups-usual care group (pamphlet-based) and interventional group (video-based), with a pre-intervention assessment using a self-administered questionnaire, followed by an educational intervention by a clinical pharmacist, and finally, a post-intervention assessment after three months. Result: Of the162 respondents, the majority were female (58%), in the age group of 26–35 (30%), with intermediate education (43%) from a middle-income family. The mean score calculated for each domain among the two groups: knowledge score (Pamphlet based-Pre: 2.26 ± 1.13; post: 3.23 ± 1.02), (Video based-Pre: 2.22 ± 1.45; post: 3.95 ± 0.89), Attitude score (Pamphlet based-Pre: 2.53 ± 1.96; post: 3.23 ± 0.9), (Video based-Pre: 2.39 ± 1.81; post: 4.21 ± 1.35), Practice score (Pamphlet based-Pre: 2.19 ± 1.02; post: 4.46 ± 1.81). A significant improvement was observed in all domains of the video-based counselling group compared to the pamphlet-based (p < 0.001). Conclusion: Clinical pharmacists can effectively help in combating growing catastrophic AMR. Newer technologies need to be deployed in healthcare to educate the unreached. This study gives an insight into the technology-supported educational tool to provide awareness to the public effectively