scholarly journals Existential Methods on Diabetes Detection using Machine Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 3034-3039
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Logistic Regression ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Literature Review ◽  
Learning Techniques ◽  
The World ◽  
Diabetes Prediction

Nowadays, a lot of research is going on in healthcare. One of the significant diseases increased all over the world is Diabetes Mellitus (DM). In this paper, the literature review is done on diabetes prediction using Machine Learning and Deep Learning techniques. Various ML algorithms are used using PIDD (Pima Indian diabetes dataset), and improved k- means using logistic regression among all algorithms achieved the highest accuracy. DL algorithms like CNN and LMST used in diabetic retinopathy images.

scholarly journals Machine Learning and Deep Learning Techniques, Features and Obstacles in the Cataract Diagnosis

2020 ◽  
Vol 9 (3) ◽  
pp. 87-93
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Congenital Cataract ◽  
Low Cost ◽  
Current Situation ◽  
Learning Techniques ◽  
Research Questions ◽  
New Research

Cataract is a degenerative condition that, according to estimations, will rise globally. Even though there are various proposals about its diagnosis, there are remaining problems to be solved. This paper aims to identify the current situation of the recent investigations on cataract diagnosis using a framework to conduct the literature review with the intention of answering the following research questions: RQ1) Which are the existing methods for cataract diagnosis? RQ2) Which are the features considered for the diagnosis of cataracts? RQ3) Which is the existing classification when diagnosing cataracts? RQ4) And Which obstacles arise when diagnosing cataracts? Additionally, a cross-analysis of the results was made. The results showed that new research is required in: (1) the classification of “congenital cataract” and, (2) portable solutions, which are necessary to make cataract diagnoses easily and at a low cost.

scholarly journals Machine learning versus logistic regression methods for 2-year mortality prognostication in a small, heterogeneous glioma database

2018 ◽  
Author(s):  
Sandip S Panesar ◽  
Rhett N D’Souza ◽  
Fang-Cheng Yeh ◽  
Juan C Fernandez-Miranda
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Machine Learning Techniques ◽  
World Health ◽  
Support Vector ◽  
Molecular Characteristics ◽  
Regression Methods ◽  
Learning Techniques ◽  
The World ◽  
Health Organization

AbstractBackgroundMachine learning (ML) is the application of specialized algorithms to datasets for trend delineation, categorization or prediction. ML techniques have been traditionally applied to large, highly-dimensional databases. Gliomas are a heterogeneous group of primary brain tumors, traditionally graded using histopathological features. Recently the World Health Organization proposed a novel grading system for gliomas incorporating molecular characteristics. We aimed to study whether ML could achieve accurate prognostication of 2-year mortality in a small, highly-dimensional database of glioma patients.MethodsWe applied three machine learning techniques: artificial neural networks (ANN), decision trees (DT), support vector machine (SVM), and classical logistic regression (LR) to a dataset consisting of 76 glioma patients of all grades. We compared the effect of applying the algorithms to the raw database, versus a database where only statistically significant features were included into the algorithmic inputs (feature selection).ResultsRaw input consisted of 21 variables, and achieved performance of (accuracy/AUC): 70.7%/0.70 for ANN, 68%/0.72 for SVM, 66.7%/0.64 for LR and 65%/0.70 for DT. Feature selected input consisted of 14 variables and achieved performance of 73.4%/0.75 for ANN, 73.3%/0.74 for SVM, 69.3%/0.73 for LR and 65.2%/0.63 for DT.ConclusionsWe demonstrate that these techniques can also be applied to small, yet highly-dimensional datasets. Our ML techniques achieved reasonable performance compared to similar studies in the literature. Though local databases may be small versus larger cancer repositories, we demonstrate that ML techniques can still be applied to their analysis, though traditional statistical methods are of similar benefit.

scholarly journals Deep Learning Technique to Predict Heart Disease using IoT Based ECG Data

2019 ◽  
Vol 9 (2) ◽  
pp. 2559-2562
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Rule Based ◽  
Learning Techniques ◽  
Learning Technique ◽  
Logistic Regression Algorithm ◽  
Target Output

Scientific Knowledge and Electronic devices are growing day by day. In this aspect, many expert systems are involved in the healthcare industry using machine learning algorithms. Deep neural networks beat the machine learning techniques and often take raw data i.e., unrefined data to calculate the target output. Deep learning or feature learning is used to focus on features which is very important and gives a complete understanding of the model generated. Existing methodology used data mining technique like rule based classification algorithm and machine learning algorithm like hybrid logistic regression algorithm to preprocess data and extract meaningful insights of data. This is, however a supervised data. The proposed work is based on unsupervised data that is there is no labelled data and deep neural techniques is deployed to get the target output. Machine learning algorithms are compared with proposed deep learning techniques using TensorFlow and Keras in the aspect of accuracy. Deep learning methodology outfits the existing rule based classification and hybrid logistic regression algorithm in terms of accuracy. The designed methodology is tested on the public MIT-BIH arrhythmia database, classifying four kinds of abnormal beats. The proposed approach based on deep learning technique offered a better performance, improving the results when compared to machine learning approaches of the state-of-the-art

Impact Analysis of Machine Learning Techniques for COVID-19 Diagnosis: A Critical Review

2021 ◽  
Vol 12 (2) ◽  
pp. 510-516
Author(s):  
Anshul, Et. al.
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Severe Acute Respiratory Syndrome ◽  
Impact Analysis ◽  
Machine Learning Techniques ◽  
X Ray ◽  
Learning Techniques ◽  
The World ◽  
Almost All

COVID-19 virus belongs to the severe acute respiratory syndrome (SARS) family raised a situation of health emergency in almost all the countries of the world. Numerous machine learning and deep learning based techniques are used to diagnose COVID positive patients using different image modalities like CT SCAN, X-RAY, or CBX, etc. This paper provides the works done in COVID-19 diagnosis, the role of ML and DL based methods to solve this problem, and presents limitations with respect to COVID-19 diagnosis.

scholarly journals A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-Rays and CT Scans Using Deep Learning and Machine Learning Methodologies

Diagnostics ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 518 ◽  
Author(s):  
Hafsa Khalid ◽  
Muzammil Hussain ◽  
Mohammed A. Al Ghamdi ◽  
Tayyaba Khalid ◽  
Khadija Khalid ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Ct Scan ◽  
Systematic Literature Review ◽  
Machine Learning Techniques ◽  
Ct Scans ◽  
X Rays ◽  
X Ray ◽  
Learning Techniques

The purpose of this research was to provide a “systematic literature review” of knee bone reports that are obtained by MRI, CT scans, and X-rays by using deep learning and machine learning techniques by comparing different approaches—to perform a comprehensive study on the deep learning and machine learning methodologies to diagnose knee bone diseases by detecting symptoms from X-ray, CT scan, and MRI images. This study will help those researchers who want to conduct research in the knee bone field. A comparative systematic literature review was conducted for the accomplishment of our work. A total of 32 papers were reviewed in this research. Six papers consist of X-rays of knee bone with deep learning methodologies, five papers cover the MRI of knee bone using deep learning approaches, and another five papers cover CT scans of knee bone with deep learning techniques. Another 16 papers cover the machine learning techniques for evaluating CT scans, X-rays, and MRIs of knee bone. This research compares the deep learning methodologies for CT scan, MRI, and X-ray reports on knee bone, comparing the accuracy of each technique, which can be used for future development. In the future, this research will be enhanced by comparing X-ray, CT-scan, and MRI reports of knee bone with information retrieval and big data techniques. The results show that deep learning techniques are best for X-ray, MRI, and CT scan images of the knee bone to diagnose diseases.

scholarly journals Deep Learning based Method for Multi-class Classification of Diabetic Retinopathy

2021 ◽  
Vol 2 (2) ◽  
pp. 1-5
Author(s):  
Komal Damodara ◽  
Keyword(s):  
Diabetes Mellitus ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Blood Sugar Level ◽  
Retinal Disease ◽  
Fundus Imaging ◽  
Retinal Blood Vessels ◽  
The World ◽  
Multi Class Classification

Diabetes mellitus is a form of diabetes with secondary microvascular complication leading to renal dysfunction and retinal loss also termed as diabetic retinopathy. Retinopathy is grave form of retinal disease. It is the leading cause of blindness in the world. Blockage of tiny minute retinal blood vessels due to the high blood sugar level is the reason why retinopathy leads to blindness or loss of vision. This study serves the purpose of deep learning-based diagnosis of Diabetic retinopathy using the fundus imaging of the eye. In this study architectures such as VGG 16 and VGG 19 are deployed in order to classify the images into 5 categories. The performance of the two models were compared. The highest accuracy is 77.67% when using the VGG 16 pre-trained model.

scholarly journals Detection of Lesions for Diabetic Retinopathy By using Machine Learning Algorithms

2020 ◽  
Vol 8 (5) ◽  
pp. 1160-1166
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Image Preprocessing ◽  
Learning Techniques ◽  
Component Extraction ◽  
Single Lesion

In this paper existing writing for computer added diagnosis (CAD) based identification of lesions that might be connected in the early finding of Diabetic Retinopathy (DR) is talked about. The recognition of sores, for example, Microaneurysms (MA), Hemorrhages (HEM) and Exudates (EX) are incorporated in this paper. A range of methodologies starting from conventional morphology to deep learning techniques have been discussed. The different strategies like hand crafted feature extraction to automated CNN based component extraction, single lesion identification to multi sore recognition have been explored. The different stages in each methods beginning from the image preprocessing to classification stage are investigated. The exhibition of the proposed strategies are outlined by various performance measurement parameters and their used data sets are tabulated. Toward the end we examined the future headings.

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