scholarly journals A Machine Learning Approach for the Diagnosis of Diabetes : A Review

2020 ◽  
pp. 509-516
Author(s):  
Pravin S. Rahate ◽  
Nikhat Raza
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Selection Process ◽  
High Sensitivity ◽  
Healthcare Services ◽  
Automated System ◽  
Retinal Images ◽  
Number Of Patients ◽  
Machine Learning Approach ◽  
Patients With Diabetes

Diabetes mellitus (DM) is a chronic disease that affects 382 million patients’ worldwide (2013 data) and is predicted to increase to as many as 592 million adults by 2035. DM is one of the major causes of blindness in young adults around the world. The most serious ocular complication of DM is diabetic retinopathy (DR).Diabetic retinopathy is the most common microvascular complication in diabetes1, for the screening of which the retinal imaging is the most widely used method due to its high sensitivity in detecting retinopathy. Prompt diagnosis is important through efficient screening. The evaluation of the severity and degree of retinopathy associated with a person having diabetes is currently performed by medical experts based on the fundus or retinal images of the patient’s eyes As the number of patients with diabetes is rapidly increasing, the number of retinal images produced by the screening programmes will also increase, which in turn introduces a large labor-intensive burden on the medical experts as well as cost to the healthcare services. Manual grading of these images to determine the severity of DR is rather slow and resource demanding. This could be alleviated with an automated system either as support for medical experts’ work or as full diagnosis tool. This labor-intensive task could greatly benefit from automatic detection using machine learning technique. Early detection and timely treatment have been shown to prevent visual loss and blindness in patients with retinal complications of diabetes. Machine learning in recent years has been the evolving, reliable and supporting tools in medical domain and has provided the greatest support for predicting disease with correct case of training and testing. The objective of this paper is to explore the work happening on the detection, progression and feature selection process for the prediction of DR and to establish the extent and depth of existing knowledge on RD prediction process.

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 Application of FURIA for Finding the Faults in a Hydraulic Brake System Using a Vibration Analysis through a Machine Learning Approach

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Alamelu Manghai T. M ◽  
Jegadeeshwaran R
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Decision Tree ◽  
Continuous Monitoring ◽  
Selection Process ◽  
Brake System ◽  
Learning Approach ◽  
Vibration Signals ◽  
Hydraulic Brake ◽  
Machine Learning Approach

Vibration-based continuous monitoring system for fault diagnosis of automobile hydraulic brake system is presented in this study. This study uses a machine learning approach for the fault diagnosis study. A hydraulic brake system test rig was fabricated. The vibration signals were acquired from the brake system under different simulated fault conditions using a piezoelectric transducer. The histogram features were extracted from the acquired vibration signals. The feature selection process was carried out using a decision tree. The selected features were classified using fuzzy unordered rule induction algorithm ( FURIA ) and Repeated Incremental Pruning to Produce Error Reduction ( RIPPER ) algorithm. The classification results of both algorithms for fault diagnosis of a hydraulic brake system were presented. Compared to RIPPER and J48 decision tree, the FURIA performs better and produced 98.73 % as the classification accuracy.

scholarly journals Simple, Mobile-based Artificial Intelligence Algorithm in the detection of Diabetic Retinopathy (SMART) study

2020 ◽  
Vol 8 (1) ◽  
pp. e000892 ◽  
Author(s):  
Bhavana Sosale ◽  
Sosale Ramachandra Aravind ◽  
Hemanth Murthy ◽  
Srikanth Narayana ◽  
Usha Sharma ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Sensitivity And Specificity ◽  
Smart Phone ◽  
High Sensitivity ◽  
Ground Truth ◽  
Cross Sectional Study ◽  
Retinal Images ◽  
Cross Sectional ◽  
Smart Study

IntroductionThe aim of this study is to evaluate the performance of the offline smart phone-based Medios artificial intelligence (AI) algorithm in the diagnosis of diabetic retinopathy (DR) using non-mydriatic (NM) retinal images.MethodsThis cross-sectional study prospectively enrolled 922 individuals with diabetes mellitus. NM retinal images (disc and macula centered) from each eye were captured using the Remidio NM fundus-on-phone (FOP) camera. The images were run offline and the diagnosis of the AI was recorded (DR present or absent). The diagnosis of the AI was compared with the image diagnosis of five retina specialists (majority diagnosis considered as ground truth).ResultsAnalysis included images from 900 individuals (252 had DR). For any DR, the sensitivity and specificity of the AI algorithm was found to be 83.3% (95% CI 80.9% to 85.7%) and 95.5% (95% CI 94.1% to 96.8%). The sensitivity and specificity of the AI algorithm in detecting referable DR (RDR) was 93% (95% CI 91.3% to 94.7%) and 92.5% (95% CI 90.8% to 94.2%).ConclusionThe Medios AI has a high sensitivity and specificity in the detection of RDR using NM retinal images.

scholarly journals CLINICAL STUDY OF RETINOPATHY IN PATIENTS WITH DIABETES MELLITUS TYPE 1 AND TYPE 2 AND ITS ASSOCIATION WITH RISK FACTORS

2019 ◽  
Vol 3 (10) ◽  
Author(s):  
Dr Kalpana Singh ◽  
Dr Dhiraj Balwir ◽  
Dr Jeetendra Singh ◽  
Dr Ruchita Raikar
Keyword(s):  
Diabetes Mellitus ◽  
Diabetic Retinopathy ◽  
Proliferative Diabetic Retinopathy ◽  
Multivariate Logistic Regression Analysis ◽  
P Value ◽  
Systemic Complications ◽  
Female Ratio ◽  
Number Of Patients ◽  
Patients With Diabetes

Aim: To study the relationship between severity of diabetic retinopathy (PDR or NPDR) and systemic complications of diabetes mellitus such as Neuropathy, Nephropathy or Cardiovascular manifestation as hypertension. Methods and Materials: This prospective observational study of 100 patients suffering from diabetic retinopathy. Such patients were recruited as a part of the study and further examined for any other systemic abnormality as neuropathy, nephropathy or hypertension. Statistical Analysis: Chi square test, univariate and multivariate logistic regression analysis was performed. P value < 0.05 was taken as significant. Results: Male: Female ratio of presence of diabetic retinopathy was 2.13: 1. The rate of proliferative diabetic retinopathy (PDR) was 1.47 % in persons who had diabetes for less than 5 years to 7.35 % in persons who had diabetes more than 15 years. In our study, it was seen that nephropathy was present in 35.71 % cases with PDR as compared to 8.93% of cases with Non proliferative diabetic retinopathy (NPDR). Conclusion: Our study showed that there is a significant correlation between severity of retinopathy and duration in type 2 Diabetes mellitus patients. Maximum number of patients with Diabetes mellitus having cardiovascular involvement, had hypertension (68%).In patients suffering from neuropathy as a complication of DM, maximum number of patients had diabetic foot (56%).It was seen that the severity of diabetic retinopathy had some association with presence of nephropathy. Also it can be postulated that patients with severe NPDR and PDR have high risk of developing nephropathy than patients suffering with mild and moderate NPDR. Hence it can be recommended that all patients of diabetes mellitus suffering from clinically significant neuropathy, nephropathy or hypertension as a complication of diabetes should always be screened for presence of retinopathy. Further studies with larger sample size are to be conducted to further look into this association. Keywords: Diabetic retinopathy, Diabetic nephropathy, diabetic neuropathy, complications

scholarly journals Automation of systematic reviews through artificial neural network modeling and machine learning: A methodological protocol report (Preprint)

2020 ◽  
Author(s):  
Gabriel Ferraz Ferreira Sr ◽  
Marcos Gonçalves Quiles Sr ◽  
Tiago Santana Nazare Sr ◽  
Solange Oliveira Rezende ◽  
Marcelo Demarzo Sr
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Systematic Review ◽  
Artificial Neural Network ◽  
Systematic Reviews ◽  
Selection Process ◽  
Automated System ◽  
Neural Network Modeling ◽  
Control Group ◽  
Artificial Neural

UNSTRUCTURED Background: A systematic review can be defined as a summary of the evidence found in the literature via a systematic search in the available scientific databases. One of the steps involved is article selection, which is typically a laborious task. Machine learning and artificial intelligence can be important tools in automating this step, thus aiding researchers. The aim of this study is to create models based on an artificial neural network system and machine learning to automate the article selection process in systematic reviews in the area of Mindfulness. Methods: The study will be performed using R programming software. The system will consist of six main steps: 1) data import; 2) exclusion of duplicates; 3) exclusion of nonarticles; 4) article reading and model creation using artificial neural networks; 5) comparison of the models; and 6) system sharing. We will choose the 10 most relevant systematic reviews published in the fields of “Mindfulness and Health Promotion” and “Orthopedics and Traumatology” (control group) to serve as a test of the effectiveness of the article selection. The final results for these two fields will be compared. Conclusion: An automated system with a modifiable sensitivity will be created to select scientific articles in systematic review that can be expanded to various fields. We will disseminate our results and models through the “Observatory of Evidence” in public health, an open and online platform that will assist researchers in systematic reviews.

scholarly journals Screening for Diabetic Retinopathy Using a Portable, Noncontact, Nonmydriatic Handheld Retinal Camera

2016 ◽  
Vol 11 (1) ◽  
pp. 128-134 ◽  
Author(s):  
Wenlan Zhang ◽  
Peter Nicholas ◽  
Stefanie Gail Schuman ◽  
Michael John Allingham ◽  
Ambar Faridi ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Prospective Study ◽  
Low Vision ◽  
High Sensitivity ◽  
Retinal Images ◽  
Training Experience ◽  
Clinical Exam ◽  
Eye Exams ◽  
A Prospective Study ◽  
Different Levels

Background: Diabetic retinopathy (DR) is a leading cause of low vision and blindness. We evaluated the feasibility of using a handheld, noncontact digital retinal camera, Pictor, to obtain retinal images in dilated and undilated eyes for DR screening. We also evaluated the accuracy of ophthalmologists with different levels of training/experience in grading these images to identify eyes with vision-threatening DR. Methods: A prospective study of diabetic adults scheduled to have dilated eye exams at Duke Eye Center from January to May 2014 was conducted. An imager acquired retinal images pre- and postdilation with Pictor and selected 1 pre- and 1 postdilation image per eye. Five masked ophthalmologists graded images for gradability (based on image focus and centration) and the presence of no, mild, moderate, or severe nonproliferative DR (NPDR) or proliferative DR (PDR). Referable disease was defined as moderate or severe NPDR or PDR on image grading. We evaluated feasibility based on the graders’ evaluation of image gradability. We evaluated accuracy of identifying vision-threatening disease (severe NPDR or PDR documented on dilated clinical examination) based on the graders’ sensitivity and specificity of grading referable disease. Results: Images were gradable in 86-94% of predilation and 94-97% of postdilation photos. Compared to the dilated clinical exam, overall sensitivity for identifying vision-threatening DR was 64-88% and specificity was 71-90%. Conclusions: Pictor can capture retinal images of sufficient quality to screen for DR with and without dilation. Single retinal images obtained using Pictor can identify eyes with vision-threatening DR with high sensitivity and acceptable specificity compared to clinical exam.

scholarly journals Diabetic retinopathy detection and classification using capsule networks

2021 ◽  
Author(s):  
G. Kalyani ◽  
B. Janakiramaiah ◽  
A. Karuna ◽  
L. V. Narasimha Prasad
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Good Prognosis ◽  
Automated System ◽  
Machine Learning Techniques ◽  
Specific Class ◽  
Fundus Images ◽  
The People ◽  
Recent Developments

AbstractNowadays, diabetic retinopathy is a prominent reason for blindness among the people who suffer from diabetes. Early and timely detection of this problem is critical for a good prognosis. An automated system for this purpose contains several phases like identification and classification of lesions in fundus images. Machine learning techniques based on manual extraction of features and automatic extraction of features with convolution neural network have been presented for diabetic retinopathy detection. The recent developments like capsule networks in deep learning and their significant success over traditional machine learning methods for a variety of applications inspired the researchers to apply them for diabetic retinopathy diagnosis. In this paper, a reformed capsule network is developed for the detection and classification of diabetic retinopathy. Using the convolution and primary capsule layer, the features are extracted from the fundus images and then using the class capsule layer and softmax layer the probability that the image belongs to a specific class is estimated. The efficiency of the proposed reformed network is validated concerning four performance measures by considering the Messidor dataset. The constructed capsule network attains an accuracy of 97.98%, 97.65%, 97.65%, and 98.64% on the healthy retina, stage 1, stage 2, and stage 3 fundus images.

scholarly journals Improving Diabetic Diagnosis and Prevention with Machine Learning on Retinal Imaging

2021 ◽  
Vol 271 ◽  
pp. 01034
Author(s):  
Yushan Min
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Diabetic Retinopathy ◽  
Learning Algorithms ◽  
Area Under The Curve ◽  
Retinal Imaging ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Retinal Images ◽  
Support Vector Machine Algorithm

If the retinal images show evidences of abnormalities such as change in volume, diameter, and unusual spots in the retina, then there is a positive correlation to the diabetic progress. Mathematical and statistical theories behind the machine learning algorithms are powerful enough to detect signs of diabetes through retinal images. Several machine learning algorithms: Logistic Regression, Support Vector Machine, Random Forest, and Neural Networks were applied to predict whether images contain signs of diabetic retinopathy or not. After building the models, the computed results of these algorithms were compared by confusion matrixes, receiver operating characteristic curves, and Precision-Recall curves. The performance of the Support Vector Machine algorithm was the best since it had the highest true-positive rate, area under the curve for ROC curve, and area under the curve for Precision-Recall curve. This conclusion shows that the most complex algorithms doesn’t always give the best performance, the final accuracy also depends on the dataset. For this dataset of retinal imaging, the Support Vector Machine algorithm achieved the best results. Detecting signs of diabetic retinopathy is helpful for detecting for diabetes since more than 60% of patients with diabetes have signs of diabetic retinopathy. Machine learning algorithms can speed up the process and improve the accuracy of diagnosis. When the method is reliable enough, it can be utilized in diabetes diagnosis directly in clinics. Current methods require going on diets and taking blood samples, which could be very time consuming and inconvenient. Using machine learning algorithms is fast and noninvasive compared to the existing methods. The purpose of this research was to build an optimized model by machine learning algorithms that can improve the diagnosis accuracy and classification of patients at high risk of diabetes using retinal imaging.

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.

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