scholarly journals Postsurgery Classification of Best-Corrected Visual Acuity Changes Based on Pterygium Characteristics Using the Machine Learning Technique

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Fatin Nabihah Jais ◽  
Mohd Zulfaezal Che Azemin ◽  
Mohd Radzi Hilmi ◽  
Mohd Izzuddin Mohd Tamrin ◽  
Khairidzan Mohd Kamal
Keyword(s):  
Machine Learning ◽  
Visual Acuity ◽  
Visual Impairment ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Machine Learning Technique ◽  
Corrected Visual Acuity ◽  
Learning Technique ◽  
Best Corrected Visual Acuity

Introduction. Early detection of visual symptoms in pterygium patients is crucial as the progression of the disease can cause visual disruption and contribute to visual impairment. Best-corrected visual acuity (BCVA) and corneal astigmatism influence the degree of visual impairment due to direct invasion of fibrovascular tissue into the cornea. However, there were different characteristics of pterygium used to evaluate the severity of visual impairment, including fleshiness, size, length, and redness. The innovation of machine learning technology in visual science may contribute to developing a highly accurate predictive analytics model of BCVA outcomes in postsurgery pterygium patients. Aim. To produce an accurate model of BCVA changes of postpterygium surgery according to its morphological characteristics by using the machine learning technique. Methodology. A retrospective of the secondary dataset of 93 samples of pterygium patients with different pterygium attributes was used and imported into four different machine learning algorithms in RapidMiner software to predict the improvement of BCVA after pterygium surgery. Results. The performance of four machine learning techniques were evaluated, and it showed the support vector machine (SVM) model had the highest average accuracy (94.44% ± 5.86%), specificity (100%), and sensitivity (92.14% ± 8.33%). Conclusion. Machine learning algorithms can produce a highly accurate postsurgery classification model of BCVA changes using pterygium characteristics.

Beatmungsassoziierte Pneumonie: Elektronische Nase und künstliche Intelligenz als Potenzial für die Zukunft

2020 ◽  
Vol 8 (5) ◽  
pp. 254-255
Author(s):  
Johannes Knoch
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Predictive Value ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
The Mean

Background: Ventilator-associated pneumonia (VAP) is a significant cause of mortality in the intensive care unit. Early diagnosis of VAP is important to provide appropriate treatment and reduce mortality. Developing a noninvasive and highly accurate diagnostic method is important. The invention of electronic sensors has been applied to analyze the volatile organic compounds in breath to detect VAP using a machine learning technique. However, the process of building an algorithm is usually unclear and prevents physicians from applying the artificial intelligence technique in clinical practice. Clear processes of model building and assessing accuracy are warranted. The objective of this study was to develop a breath test for VAP with a standardized protocol for a machine learning technique. Methods: We conducted a case-control study. This study enrolled subjects in an intensive care unit of a hospital in southern Taiwan from February 2017 to June 2019. We recruited patients with VAP as the case group and ventilated patients without pneumonia as the control group. We collected exhaled breath and analyzed the electric resistance changes of 32 sensor arrays of an electronic nose. We split the data into a set for training algorithms and a set for testing. We applied eight machine learning algorithms to build prediction models, improving model performance and providing an estimated diagnostic accuracy. Results: A total of 33 cases and 26 controls were used in the final analysis. Using eight machine learning algorithms, the mean accuracy in the testing set was 0.81 ± 0.04, the sensitivity was 0.79 ± 0.08, the specificity was 0.83 ± 0.00, the positive predictive value was 0.85 ± 0.02, the negative predictive value was 0.77 ± 0.06, and the area under the receiver operator characteristic curves was 0.85 ± 0.04. The mean kappa value in the testing set was 0.62 ± 0.08, which suggested good agreement. Conclusions: There was good accuracy in detecting VAP by sensor array and machine learning techniques. Artificial intelligence has the potential to assist the physician in making a clinical diagnosis. Clear protocols for data processing and the modeling procedure needed to increase generalizability.

scholarly journals Computer Vision Based Detection and Quantification of Extraneous Water in Raw Milk

2021 ◽  
Author(s):  
Bezuayehu Gutema Asefa ◽  
Legesse Hagos ◽  
Tamirat Kore ◽  
Shimelis Admassu Emire
Keyword(s):  
Machine Learning ◽  
Digital Image Analysis ◽  
Raw Milk ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Machine Learning Technique ◽  
Milk Adulteration ◽  
Learning Technique ◽  
Total Accuracy

Abstract A rapid method based on digital image analysis and machine learning technique is proposed for the detection of milk adulteration with water. Several machine learning algorithms were compared, and SVM performed best with 89.48 % of total accuracy and 95.10 % precision. An increase in the classification performance was observed in extreme classes. Better quantitative determination of the extraneous water was achieved using SVMR with R2(CV) and R2(P) of 0.65 and 0.71 respectively. The proposed technique can be used to screen raw milk based on the level of added extraneous water without the necessity of any additional reagent.

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

scholarly journals A Self-Supervised Machine Learning Approach for Objective Live Cell Segmentation and Analysis

2021 ◽  
Author(s):  
Marc Raphael ◽  
Michael Robitaille ◽  
Jeff Byers ◽  
Joseph Christodoulides
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Label Cell ◽  
Live Cell ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Cell Segmentation ◽  
Data Set

Abstract Machine learning algorithms hold the promise of greatly improving live cell image analysis by way of (1) analyzing far more imagery than can be achieved by more traditional manual approaches and (2) by eliminating the subjective nature of researchers and diagnosticians selecting the cells or cell features to be included in the analyzed data set. Currently, however, even the most sophisticated model based or machine learning algorithms require user supervision, meaning the subjectivity problem is not removed but rather incorporated into the algorithm’s initial training steps and then repeatedly applied to the imagery. To address this roadblock, we have developed a self-supervised machine learning algorithm that recursively trains itself directly from the live cell imagery data, thus providing objective segmentation and quantification. The approach incorporates an optical flow algorithm component to self-label cell and background pixels for training, followed by the extraction of additional feature vectors for the automated generation of a cell/background classification model. Because it is self-trained, the software has no user-adjustable parameters and does not require curated training imagery. The algorithm was applied to automatically segment cells from their background for a variety of cell types and five commonly used imaging modalities - fluorescence, phase contrast, differential interference contrast (DIC), transmitted light and interference reflection microscopy (IRM). The approach is broadly applicable in that it enables completely automated cell segmentation for long-term live cell phenotyping applications, regardless of the input imagery’s optical modality, magnification or cell type.

scholarly journals A Self-Supervised Machine Learning Approach for Objective Live Cell Segmentation and Analysis

2021 ◽  
Author(s):  
Michael C. Robitaille ◽  
Jeff M. Byers ◽  
Joseph A. Christodoulides ◽  
Marc P. Raphael
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Label Cell ◽  
Live Cell ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Cell Segmentation ◽  
Data Set

Machine learning algorithms hold the promise of greatly improving live cell image analysis by way of (1) analyzing far more imagery than can be achieved by more traditional manual approaches and (2) by eliminating the subjective nature of researchers and diagnosticians selecting the cells or cell features to be included in the analyzed data set. Currently, however, even the most sophisticated model based or machine learning algorithms require user supervision, meaning the subjectivity problem is not removed but rather incorporated into the algorithm's initial training steps and then repeatedly applied to the imagery. To address this roadblock, we have developed a self-supervised machine learning algorithm that recursively trains itself directly from the live cell imagery data, thus providing objective segmentation and quantification. The approach incorporates an optical flow algorithm component to self-label cell and background pixels for training, followed by the extraction of additional feature vectors for the automated generation of a cell/background classification model. Because it is self-trained, the software has no user-adjustable parameters and does not require curated training imagery. The algorithm was applied to automatically segment cells from their background for a variety of cell types and five commonly used imaging modalities - fluorescence, phase contrast, differential interference contrast (DIC), transmitted light and interference reflection microscopy (IRM). The approach is broadly applicable in that it enables completely automated cell segmentation for long-term live cell phenotyping applications, regardless of the input imagery's optical modality, magnification or cell type.

scholarly journals Machine learning in the diagnosis of Myocardial Infarction with Non-Obstructive Coronary Arteries

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
M J Espinosa Pascual ◽  
P Vaquero Martinez ◽  
V Vaquero Martinez ◽  
J Lopez Pais ◽  
B Izquierdo Coronel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Random Forest ◽  
Obstructive Coronary Artery Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector

Abstract Introduction Out of all patients admitted with Myocardial Infarction, 10 to 15% have Myocardial Infarction with Non-Obstructive Coronaries Arteries (MINOCA). Classification algorithms based on deep learning substantially exceed traditional diagnostic algorithms. Therefore, numerous machine learning models have been proposed as useful tools for the detection of various pathologies, but to date no study has proposed a diagnostic algorithm for MINOCA. Purpose The aim of this study was to estimate the diagnostic accuracy of several automated learning algorithms (Support-Vector Machine [SVM], Random Forest [RF] and Logistic Regression [LR]) to discriminate between people suffering from MINOCA from those with Myocardial Infarction with Obstructive Coronary Artery Disease (MICAD) at the time of admission and before performing a coronary angiography, whether invasive or not. Methods A Diagnostic Test Evaluation study was carried out applying the proposed algorithms to a database constituted by 553 consecutive patients admitted to our Hospital with Myocardial Infarction. According to the definitions of 2016 ESC Position Paper on MINOCA, patients were classified into two groups: MICAD and MINOCA. Out of the total 553 patients, 214 were discarded due to the lack of complete data. The set of machine learning algorithms was trained on 244 patients (training sample: 75%) and tested on 80 patients (test sample: 25%). A total of 64 variables were available for each patient, including demographic, clinical and laboratorial features before the angiographic procedure. Finally, the diagnostic precision of each architecture was taken. Results The most accurate classification model was the Random Forest algorithm (Specificity [Sp] 0.88, Sensitivity [Se] 0.57, Negative Predictive Value [NPV] 0.93, Area Under the Curve [AUC] 0.85 [CI 0.83–0.88]) followed by the standard Logistic Regression (Sp 0.76, Se 0.57, NPV 0.92 AUC 0.74 and Support-Vector Machine (Sp 0.84, Se 0.38, NPV 0.90, AUC 0.78) (see graph). The variables that contributed the most in order to discriminate a MINOCA from a MICAD were the traditional cardiovascular risk factors, biomarkers of myocardial injury, hemoglobin and gender. Results were similar when the 19 patients with Takotsubo syndrome were excluded from the analysis. Conclusion A prediction system for diagnosing MINOCA before performing coronary angiographies was developed using machine learning algorithms. Results show higher accuracy of diagnosing MINOCA than conventional statistical methods. This study supports the potential of machine learning algorithms in clinical cardiology. However, further studies are required in order to validate our results. FUNDunding Acknowledgement Type of funding sources: None. ROC curves of different algorithms

Efficient classification model of web news documents using machine learning algorithms for accurate information

2020 ◽  
Vol 98 ◽  
pp. 102006
Author(s):  
Aos Mulahuwaish ◽  
Kevin Gyorick ◽  
Kayhan Zrar Ghafoor ◽  
Halgurd S. Maghdid ◽  
Danda B. Rawat
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Accurate Information ◽  
Web News

A Review of Classification and Novel Class Detection Technique of Data Streams

2012 ◽  
Vol 3 (2) ◽  
pp. 314-316
Author(s):  
Manish Rai ◽  
Rekha Pandit
Keyword(s):  
Machine Learning ◽  
Data Streams ◽  
Concept Drift ◽  
Data Classification ◽  
Classification Model ◽  
Infinite Length ◽  
Stream Data ◽  
Machine Learning Technique ◽  
Feature Evaluation ◽  
Learning Technique

Stream data classification suffered from a problem of infinite length, concept evaluation, feature evaluation and data drift. Data stream labeling is more challenging than label static data because of several unique properties of data streams. Data streams are suppose to have infinite length, which makes it difficult to store and use all the historical data for training. Earlier multi-pass machine learning technique is not directly applied to data streams. Data streams discover concept-drift, which occurs when the discontinue concept of the data changes over time. In order to address concept drift, a classification model must endlessly adapt itself to the most recent concept. Various authors reduce these problem using machine learning approach and feature optimization technique. In this paper we present various method for reducing such problem occurred in stream data classification. Here we also discuss a machine learning technique for feature evaluation process for generation of novel class.

Breast cancer prediction using an optimal machine learning technique for next generation sequences

2021 ◽  
pp. 1063293X2199180
Author(s):  
Babymol Kurian ◽  
VL Jyothi
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Decision Tree ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Next Generation ◽  
Machine Learning Technique ◽  
Cancer Prediction ◽  
Learning Technique

A wide reach on cancer prediction and detection using Next Generation Sequencing (NGS) by the application of artificial intelligence is highly appreciated in the current scenario of the medical field. Next generation sequences were extracted from NCBI (National Centre for Biotechnology Information) gene repository. Sequences of normal Homo sapiens (Class 1), BRCA1 (Class 2) and BRCA2 (Class 3) were extracted for Machine Learning (ML) purpose. The total volume of datasets extracted for the process were 1580 in number under four categories of 50, 100, 150 and 200 sequences. The breast cancer prediction process was carried out in three major steps such as feature extraction, machine learning classification and performance evaluation. The features were extracted with sequences as input. Ten features of DNA sequences such as ORF (Open Reading Frame) count, individual nucleobase average count of A, T, C, G, AT and GC-content, AT/GC composition, G-quadruplex occurrence, MR (Mutation Rate) were extracted from three types of sequences for the classification process. The sequence type was also included as a target variable to the feature set with values 0, 1 and 2 for classes 1, 2 and 3 respectively. Nine various supervised machine learning techniques like LR (Logistic Regression statistical model), LDA (Linear Discriminant analysis model), k-NN (k nearest neighbours’ algorithm), DT (Decision tree technique), NB (Naive Bayes classifier), SVM (Support-Vector Machine algorithm), RF (Random Forest learning algorithm), AdaBoost (AB) and Gradient Boosting (GB) were employed on four various categories of datasets. Of all supervised models, decision tree machine learning technique performed most with maximum accuracy in classification of 94.03%. Classification model performance was evaluated using precision, recall, F1-score and support values wherein F1-score was most similar to the classification accuracy.

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