scholarly journals Machine Learning Approach for Confirmation of COVID-19 Cases: Positive, Negative, Death and Release

2020 ◽  
Author(s):  
Shawni Dutta ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Original Data ◽  
High Rate ◽  
Learning Approach ◽  
Accurate Identification ◽  
Verification Method ◽  
Number Of Patients ◽  
Machine Learning Approach ◽  
Deep Learning Neural Network

AbstractIn recent days, Covid-19 coronavirus has been an immense impact on social, economic fields in the world. The objective of this study determines if it is feasible to use machine learning method to evaluate how much prediction results are close to original data related to Confirmed-Negative-Released-Death cases of Covid-19. For this purpose, a verification method is proposed in this paper that uses the concept of Deep-learning Neural Network. In this framework, Long short-term memory (LSTM) and Gated Recurrent Unit (GRU) are also assimilated finally for training the dataset and the prediction results are tally with the results predicted by clinical doctors. The prediction results are validated against the original data based on some predefined metric. The experimental results showcase that the proposed approach is useful in generating suitable results based on the critical disease outbreak. It also helps doctors to recheck further verification of virus by the proposed method. The outbreak of Coronavirus has the nature of exponential growth and so it is difficult to control with limited clinical persons for handling a huge number of patients with in a reasonable time. So it is necessary to build an automated model, based on machine learning approach, for corrective measure after the decision of clinical doctors. It could be a promising supplementary confirmation method for frontline clinical doctors. The proposed method has a high prediction rate and works fast for probable accurate identification of the disease. The performance analysis shows that a high rate of accuracy is obtained by the proposed method.

Machine Learning Approach for Confirmation of COVID-19 Cases: Positive, Negative, Death and Release (Preprint)

2020 ◽  
Author(s):  
Samir Bandyopadhyay Sr ◽  
SHAWNI DUTTA ◽  
SHAWNI DUTTA ◽  
SHAWNI DUTTA
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Health Workers ◽  
Original Data ◽  
High Rate ◽  
Learning Approach ◽  
Good Decision ◽  
Number Of Patients ◽  
Machine Learning Approach ◽  
Death Cases

BACKGROUND In recent days, Covid-19 coronavirus has been an immense impact on social, economic fields in the world. The objective of this study determines if it is feasible to use machine learning method to evaluate how much prediction results are close to original data related to Confirmed-Negative-Released-Death cases of Covid-19. For this purpose, a verification method is proposed in this paper that uses the concept of Deep-learning Neural Network. In this framework, Long short-term memory (LSTM) and Gated Recurrent Unit (GRU) are also assimilated finally for training the dataset and the prediction results are tally with the results predicted by clinical doctors. The prediction results are validated against the original data based on some predefined metric. The experimental results showcase that the proposed approach is useful in generating suitable results based on the critical disease outbreak. It also helps doctors to recheck further verification of virus by the proposed method. The outbreak of Coronavirus has the nature of exponential growth and so it is difficult to control with limited clinical persons for handling a huge number of patients with in a reasonable time. So it is necessary to build an automated model, based on machine learning approach, for corrective measure after the decision of clinical doctors. It could be a promising supplementary confirmation method for frontline clinical doctors. The proposed method has a high prediction rate and works fast for probable accurate identification of the disease. The performance analysis shows that a high rate of accuracy is obtained by the proposed method. OBJECTIVE Validation of COVID-19 disease METHODS Machine Learning RESULTS 90% CONCLUSIONS The combined LSTM-GRU based RNN model provides a comparatively better results in terms of prediction of confirmed, released, negative, death cases on the data. This paper presented a novel method that could recheck occurred cases of COVID-19 automatically. The data driven RNN based model is capable of providing automated tool for confirming, estimating the current position of this pandemic, assessing the severity, and assisting government and health workers to act for good decision making policy. It could be a promising supplementary rechecking method for frontline clinical doctors. It is now essential for improving the accuracy of detection process. CLINICALTRIAL 2020-04-03 3:22:36 PM

scholarly journals Machine learning approach for confirmation of COVID-19 cases: positive, negative, death and release

2020 ◽  
Vol 2 (3) ◽  
pp. 172-177
Author(s):  
Shawni Dutta ◽  
◽  
Samir Kumar Bandyopadhyay ◽  
Keyword(s):  
Infectious Disease ◽  
Machine Learning ◽  
Original Data ◽  
World Health ◽  
Learning Approach ◽  
Human Beings ◽  
Verification Method ◽  
Number Of Patients ◽  
Machine Learning Approach ◽  
The World

Introduction: Corona Virus Infectious Disease (COVID-19) is the infectious disease. The COVID-19 disease came to earth in early 2019. It is expanding exponentially throughout the world and affected an enormous number of human beings starting from the last month. The World Health Organization (WHO) on March 11, 2020 declared COVID-19 was characterized as “Pandemic”. This paper proposed approach for confirmation of COVID-19 cases after the diagnosis of doctors. The objective of this study uses machine learning method to evaluate how much predicted results are close to original data related to Confirmed-Negative-Released-Death cases of COVID-19. Materials and methods: For this purpose, a verification method is proposed in this paper that uses the concept of Deep-learning Neural Network. In this framework, Long shrt-term memory (LSTM) and Gated Recurrent Unit (GRU) are also assimilated finally for training the dataset. The prediction results are tally with the results predicted by clinical doctors. Results: The results are obtained from the proposed method with accuracy 87 % for the “confirmed Cases”, 67.8 % for “Negative Cases”, 62% for “Deceased Case” and 40.5 % for “Released Case”. Another important parameter i.e. RMSE shows 30.15% for Confirmed Case, 49.4 % for Negative Cases, 4.16 % for Deceased Case and 13.72 % for Released Case. Conclusions: The outbreak of Coronavirus has the nature of exponential growth and so it is difficult to control with limited clinical persons for handling a huge number of patients within a reasonable time. So it is necessary to build an automated model, based on machine learning approach, for corrective measure after the decision of clinical doctors.

scholarly journals A Machine Learning Approach for the Classification of Kidney Cancer Subtypes Using miRNA Genome Data

2018 ◽  
Vol 8 (12) ◽  
pp. 2422 ◽  
Author(s):  
Ali Muhamed Ali ◽  
Hanqi Zhuang ◽  
Ali Ibrahim ◽  
Oneeb Rehman ◽  
Michelle Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Kidney Cancer ◽  
Short Term Memory ◽  
The Cancer Genome Atlas ◽  
Data Repository ◽  
Learning Approach ◽  
Cancer Subtypes ◽  
Genome Data ◽  
Machine Learning Approach

Kidney cancer is one of the deadliest diseases and its diagnosis and subtype classification are crucial for patients’ survival. Thus, developing automated tools that can accurately determine kidney cancer subtypes is an urgent challenge. It has been confirmed by researchers in the biomedical field that miRNA dysregulation can cause cancer. In this paper, we propose a machine learning approach for the classification of kidney cancer subtypes using miRNA genome data. Through empirical studies we found 35 miRNAs that possess distinct key features that aid in kidney cancer subtype diagnosis. In the proposed method, Neighbourhood Component Analysis (NCA) is employed to extract discriminative features from miRNAs and Long Short Term Memory (LSTM), a type of Recurrent Neural Network, is adopted to classify a given miRNA sample into kidney cancer subtypes. In the literature, only a couple of kidney subtypes have been considered for classification. In the experimental study, we used the miRNA quantitative read counts data, which was provided by The Cancer Genome Atlas data repository (TCGA). The NCA procedure selected 35 of the most discriminative miRNAs. With this subset of miRNAs, the LSTM algorithm was able to group kidney cancer miRNAs into five subtypes with average accuracy around 95% and Matthews Correlation Coefficient value around 0.92 under 10 runs of randomly grouped 5-fold cross-validation, which were very close to the average performance of using all miRNAs for classification.

scholarly journals Consideration of Passenger Interactions for the Prediction of Aircraft Boarding Time

Aerospace ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 101 ◽  
Author(s):  
Michael Schultz ◽  
Stefan Reitmann
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Learning Approach ◽  
Neural Network Approach ◽  
Machine Learning Approach ◽  
Long Short Term Memory ◽  
Status Data ◽  
Passenger Behavior ◽  
The Given ◽  
Operational Data

In this paper we address the prediction of aircraft boarding using a machine learning approach. Reliable process predictions of aircraft turnaround are an important element to further increase the punctuality of airline operations. In this context, aircraft turnaround is mainly controlled by operational experts, but the critical aircraft boarding is driven by the passengers’ experience and willingness or ability to follow the proposed procedures. Thus, we used a developed complexity metric to evaluate the actual boarding progress and a machine learning approach to predict the final boarding time during running operations. A validated passenger boarding model is used to provide reliable aircraft status data, since no operational data are available today. These data are aggregated to a time-based complexity value and used as input for our recurrent neural network approach for predicting the boarding progress. In particular we use a Long Short-Term Memory model to learn the dynamical passenger behavior over time with regards to the given complexity metric.

scholarly journals COVID-19 Future Predictions Using Machine Learning Algorithms

2021 ◽  
pp. 102-110
Author(s):  
R. Saradha Devi ◽  
Dr. J. G. R. Sathiaseelan
Keyword(s):  
Infectious Disease ◽  
Machine Learning ◽  
Short Term Memory ◽  
Original Data ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Human Beings ◽  
The World ◽  
Health Organization ◽  
Deep Learning Neural Network

Corona Virus Infectious Disease (COVID-19) is an infectious disease. The COVID-19 disease came to earth in early 2019. It is expanding exponentially throughout the world and affected an enormous number of human beings starting from the last year. COVID-19 was declared “Pandemic” by the World Health Organization (WHO) on March 11, 2020. This research proposed a method for confirming COVID-19 instances after doctors' diagnoses. The goal of this study is to see how similar the projected findings are to the original data in COVID-19 Confirmed-Negative-Released-Death situations using machine learning. This paper suggests a verification approach created on the Deep-learning Neural Network concept for this purpose. Long short-term memory (LSTM) and Gated Recurrent Unit (GRU) are also used in this framework to train the dataset. The outcomes of the forecast match those predicted by clinical doctors.

scholarly journals Identification of a Transcriptomic Prognostic Signature by Machine Learning Using a Combination of Small Cohorts of Prostate Cancer

2020 ◽  
Vol 11 ◽  
Author(s):  
Benjamin Vittrant ◽  
Mickael Leclercq ◽  
Marie-Laure Martin-Magniette ◽  
Colin Collins ◽  
Alain Bergeron ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Specific Antigen ◽  
Gene Signature ◽  
Learning Approach ◽  
Gleason Grade ◽  
Rna Seq ◽  
Number Of Patients ◽  
Heterogeneous Datasets ◽  
Machine Learning Approach

Determining which treatment to provide to men with prostate cancer (PCa) is a major challenge for clinicians. Currently, the clinical risk-stratification for PCa is based on clinico-pathological variables such as Gleason grade, stage and prostate specific antigen (PSA) levels. But transcriptomic data have the potential to enable the development of more precise approaches to predict evolution of the disease. However, high quality RNA sequencing (RNA-seq) datasets along with clinical data with long follow-up allowing discovery of biochemical recurrence (BCR) biomarkers are small and rare. In this study, we propose a machine learning approach that is robust to batch effect and enables the discovery of highly predictive signatures despite using small datasets. Gene expression data were extracted from three RNA-Seq datasets cumulating a total of 171 PCa patients. Data were re-analyzed using a unique pipeline to ensure uniformity. Using a machine learning approach, a total of 14 classifiers were tested with various parameters to identify the best model and gene signature to predict BCR. Using a random forest model, we have identified a signature composed of only three genes (JUN, HES4, PPDPF) predicting BCR with better accuracy [74.2%, balanced error rate (BER) = 27%] than the clinico-pathological variables (69.2%, BER = 32%) currently in use to predict PCa evolution. This score is in the range of the studies that predicted BCR in single-cohort with a higher number of patients. We showed that it is possible to merge and analyze different small and heterogeneous datasets altogether to obtain a better signature than if they were analyzed individually, thus reducing the need for very large cohorts. This study demonstrates the feasibility to regroup different small datasets in one larger to identify a predictive genomic signature that would benefit PCa patients.

A data-based machine learning approach for RPC time resolution study based on ToF reconstruction

2021 ◽  
Vol 16 (12) ◽  
pp. P12002
Author(s):  
X.Y. Xie ◽  
H.L. Xu ◽  
Q.Y. Li ◽  
Y.J. Sun
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Performance Studies ◽  
Time Resolution ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Waveform Analysis ◽  
Learning Approach ◽  
The Neural Network ◽  
Machine Learning Approach

Abstract A data-based machine learning approach is proposed to study the properties of time resolution of RPC detectors by measuring the time of flight of cosmic muons. This method utilises a multi-layer perceptron and a type of recurrent neural network called long short-term memory. The neural network is trained with the waveforms of RPC signals digitized by an oscilloscope at a sampling frequency of 10 GHz and a 2 GHz bandwidth. A data augmentation approach is implemented for labelling. Compared to the results from conventional waveform analysis, this approach achieves a better time resolution of 1-mm gap RPCs. Based on the data, the approach has a generalisation capacity for performance studies of other timing detectors.

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