Notice of Retraction: Molecular Diagnostic and Using Deep Learning Techniques for Predict Functional Recovery of Patients Treated of Cardiovascular Disease

Abstract

Today, with the development of industry and mechanized life style, the prevalence of the disease is rising steadily as well. Observing at the trend and lifecycle style, its predict that after ten years around 23.6 million people die because of Cardiovascular Disease (CVD). For that reason, aim to use Deep Learning Techniques (DLTs), to analysis stable CVD that would give valuable awareness to decrease misdiagnosis in the Robust Healthcare Industry (RHI). An objective of this paper is first, Molecular diagnosis (MD), and second using Deep Learning Techniques DLTs, to synthesis and characterize to accumulate (raw information) from CVD patients, those who admitted the emergency section between January (2018 to December 2019). We are using Artificial Neural Network (ANN), model characterize to predict CVD patients and configuration, Feature selection (FS), Mean Square Error (MSE), accuracy, sensitivity. The ANN accuracy is 98.4, K-nearest neighbor (KNN) accuracy is 98.01%, Naïve Bayes (NB), accuracy is 96.99%. Decision tree (DT), accuracy is 87.81%. Our robust data driven model explore the efficient accuracy rate to predict CVD patients. The ANN model in term of their efficient in disease analysis, and prognosis of the RHI. INDEX TERMS Artificial neural network (ANN), cardiovascular disease (CVD), molecular diagnostic (MD), Robust Healthcare Industry (RHI).

Recursion Enhanced Random Forest With an Improved Linear Model (RERF-ILM) for Heart Disease Detection on the Internet of Medical Things Platform

“…The author introduced a new prediction model for ECG signaling in two phases, which identifies significant abnormalities (red alarms) through the comparison of signals with a Global Classifier (GC). The proposed method has less predictive accuracy and has a special benefit to predictive analyzes by creating warning messages on the high risk of heart defects for medical care [21].…”

Enhanced Deep Learning Assisted Convolutional Neural Network for Heart Disease Prediction on the Internet of Medical Things Platform

“…In General, [10], Cardiovascular disease is a term for many types, including rheumatic, coronary, and congenital heart disease. Hence, Heart activity has been analyzed during exercise, resting, and working [11,12]. Coronary artery illness signs include chest pain, discomfort, respiratory shortness, sweatiness, heart palpitation, dizziness, and fatigue.…”

Measurement of Objective Video Quality in Social Cloud Based on Reference Metric

“…Users have always high expectations and demands for qualities of video. During the video transmission and online streaming, a single video may experience jitter or additional noise by uploading/downloading of videos on the cloud because the SC compresses the original video and reduces the storage size that automatically a ects the video quality [4,5]. e unnecessary noise increases the problem for MSP and reduces the provision of high de nition (HD), highquality video services agreeing to the user demands.…”

“…Subjective QoE is carried out by surveys (e.g., scale rate, interviews, and questionnaires). Objective QoE, on the other hand, carries out human physiological tests (e.g., MRI and EEG) and measures QoS data or technical parameters (e.g., cost, resolution, frame, and sampling rates) [4][5][6].…”

“…ese services are generally used by end-users to share their recorded videos on social media [2]. End users share and upload their HD video on different SC, but the original video compressed by SC does not form the original video because the social cloud stores the video by using predefined compression techniques of different video codecs [3][4][5].…”