scholarly journals Analysis of Data for Diabetics Patient

2017 ◽  
Vol 13 (15) ◽  
pp. 216
Author(s):  
Korobi Saha Koli ◽  
Sajjad Waheed
Keyword(s):  
Heart Attack ◽  
Diabetes Patient ◽  
High Accuracy ◽  
The Body ◽  
Classification Algorithms ◽  
Hoeffding Tree ◽  
Endocrine Hormone ◽  
Sugar Levels ◽  
Hormone System

Diabetes, a disease responsible for different kinds of diseases such as heart attack, kidney disease, blindness and renal failure etc. The most common disorder is the endocrine (hormone) system, occurs when blood sugar levels in the body consistently stay above normal. There are two types of diabetic; one is body's inability to make insulin and another is body not responding to the effects of insulin. In our developing country Bangladesh, Diabetes is a costly disease whose risk is increasing at alarming rate. This paper evaluates the selected classification algorithms for the classification of some Diabetes patient datasets. Classification algorithms considered here are Naive Bayes classification (NBC), Bagging algorithm, KStar algorithm, Logistic algorithm and Hoeffding tree. These algorithms are evaluated based on four criteria: Accuracy, Precision, Sensitivity and Specificity. Collected datasets of diabetes affected people are firstly preprocessed then some investigation based on mentioned algorithm has been executed successfully. From the investigation result it is found that, KStar algorithm is the best as it gives high accuracy with the low error. Here it is said that, some parameters are responsible for diabetes.

A MACHINE LEARNING APPROACH BASED ON SVM FOR CLASSIFICATION OF LIVER DISEASES

2020 ◽  
Vol 32 (03) ◽  
pp. 2050018
Author(s):  
Mohammad Fathi ◽  
Mohammadreza Nemati ◽  
Seyed Mohsen Mohammadi ◽  
Reza Abbasi-Kesbi
Keyword(s):  
Cross Validation ◽  
The Body ◽  
Support Vector ◽  
Classification Algorithms ◽  
Data Partition ◽  
Linear Quadratic ◽  
Machine Learning Approach ◽  
And Performance ◽  
Fold Cross Validation

The liver is an organ in the body that plays an important role in the production and secretion of the bile. Recently, the number of liver patients are increasing because of the inhalation of harmful gases, the consumption of contaminated foods, herbs, and narcotics. Today, classification algorithms are widely used in diverse medical applications. In this paper, the classification of the liver, and non-liver patients is performed based on a support vector machine (SVM) on two datasets. To this end, the dataset is normalized and then sorted based on a proposed algorithm. After that, the feature selection is performed in order to remove the outliers and missing data. Then, 10-fold cross-validation is used for the data partition. In the end, the classification models of Linear, Quadratic and Gaussian SVM are defined and performance evaluation of the proposed method is investigated by calculation of F1-score, accuracy, and sensitivity. The results show that ILPD data have maximum accuracy, sensitivity, and F1-score of 90.9%, 89.2%, and 94%, respectively, so that a minimum improvement of 17.9% is obtained in accuracy than previous works. Additionally, the highest accuracy, sensitivity, and F1-score of BUPA data is 92.2%, 89%, and 94.3%, separately.

scholarly journals Load Position and Weight Classification during Carrying Gait Using Wearable Inertial and Electromyographic Sensors

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4963
Author(s):  
Maja Goršič ◽  
Boyi Dai ◽  
Domen Novak
Keyword(s):  
Wearable Sensors ◽  
High Accuracy ◽  
Assistive Devices ◽  
Supervised Machine Learning ◽  
Classification Algorithms ◽  
Proof Of Concept ◽  
Side Load ◽  
Load Asymmetry

Lifting and carrying heavy objects is a major aspect of physically intensive jobs. Wearable sensors have previously been used to classify different ways of picking up an object, but have seen only limited use for automatic classification of load position and weight while a person is walking and carrying an object. In this proof-of-concept study, we thus used wearable inertial and electromyographic sensors for offline classification of different load positions (frontal vs. unilateral vs. bilateral side loads) and weights during gait. Ten participants performed 19 different carrying trials each while wearing the sensors, and data from these trials were used to train and evaluate classification algorithms based on supervised machine learning. The algorithms differentiated between frontal and other loads (side/none) with an accuracy of 100%, between frontal vs. unilateral side load vs. bilateral side load with an accuracy of 96.1%, and between different load asymmetry levels with accuracies of 75–79%. While the study is limited by a lack of electromyographic sensors on the arms and a limited number of load positions/weights, it shows that wearable sensors can differentiate between different load positions and weights during gait with high accuracy. In the future, such approaches could be used to control assistive devices or for long-term worker monitoring in physically demanding occupations.

Optimization of K Value in KNN Algorithm for Spam and Ham Email Classification

2020 ◽  
Vol 4 (2) ◽  
pp. 377-383
Author(s):  
Eko Laksono ◽  
Achmad Basuki ◽  
Fitra Bachtiar
Keyword(s):  
Frequency Distribution ◽  
Confusion Matrix ◽  
High Accuracy ◽  
K Value ◽  
A Value ◽  
Optimal Value ◽  
Classification Evaluation ◽  
Email Spam ◽  
Email Classification

There are many cases of email abuse that have the potential to harm others. This email abuse is commonly known as spam, which contains advertisements, phishing scams, and even malware. This study purpose to know the classification of email spam with ham using the KNN method as an effort to reduce the amount of spam. KNN can classify spam or ham in an email by checking it using a different K value approach. The results of the classification evaluation using confusion matrix resulted in the KNN method with a value of K = 1 having the highest accuracy value of 91.4%. From the results of the study, it is known that the optimization of the K value in KNN using frequency distribution clustering can produce high accuracy of 100%, while k-means clustering produces an accuracy of 99%. So based on the results of the existing accuracy values, the frequency distribution clustering and k-means clustering can be used to optimize the K-optimal value of the KNN in the classification of existing spam emails.

Three new species, two new genera, and new family Biphragmosagittidae (Сhaetognatha) from Southwest Pacific Ocean

2011 ◽  
Vol 20 (1) ◽  
pp. 161-173
Author(s):  
A.P. Kassatkina
Keyword(s):  
New Species ◽  
Type Species ◽  
Morphological Characters ◽  
The Body ◽  
New Order ◽  
New Genera ◽  
New Family ◽  
The Family ◽  
Three New Species

Resuming published and own data, a revision of classification of Chaetognatha is presented. The family Sagittidae Claus & Grobben, 1905 is given a rank of subclass, Sagittiones, characterised, in particular, by the presence of two pairs of sac-like gelatinous structures or two pairs of fins. Besides the order Aphragmophora Tokioka, 1965, it contains the new order Biphragmosagittiformes ord. nov., which is a unique group of Chaetognatha with an unusual combination of morphological characters: the transverse muscles present in both the trunk and the tail sections of the body; the seminal vesicles simple, without internal complex compartments; the presence of two pairs of lateral fins. The only family assigned to the new order, Biphragmosagittidae fam. nov., contains two genera. Diagnoses of the two new genera, Biphragmosagitta gen. nov. (type species B. tarasovi sp. nov. and B. angusticephala sp. nov.) and Biphragmofastigata gen. nov. (type species B. fastigata sp. nov.), detailed descriptions and pictures of the three new species are presented.

scholarly journals Cardiovascular risks awareness in coaches and amateur athletes

2021 ◽  
Vol 28 (Supplement_1) ◽  
Author(s):  
A Markovich ◽  
O Mironova
Keyword(s):  
Physical Activity ◽  
Cardiovascular Diseases ◽  
Heart Attack ◽  
Conflicts Of Interest ◽  
The Body ◽  
Treadmill Test ◽  
Cardiovascular Risks ◽  
Sports Cardiology ◽  
Long Distance ◽  
Endurance Sport

Abstract Funding Acknowledgements Type of funding sources: None. Background Regular physical activity is an important component of therapy for most сardiovascular diseases and is associated with reduced cardiovascular and all-cause mortality. The promotion of the physical activity and regular exercise is an important preventive measure that affects the prognosis. Purpose To assess the awareness of the prevalence of cardiovascular disease in exercising population and its influence on the safety of the patients and healthy adults among coaches and people actively engaged in sports activities. Methods An open non-randomized observation was conducted. The questionnaire created by our team included 45 questions about cardiovascular diseases and sport. 111 athletes and coaches aged from 19 to 46 were enrolled in the study. 61,5% (68) are men and 38,5% (42) of the respondents are women. 30,3% (33) of the respondents are coaches. 45,5% (15) of them have over 5 years of coaching experience. 44% (48) of all respondents prefer endurance sport, 25,7% (28) train strength exercise. 63,6% (70) train 3-8 hours per week, 12,7% (14 [7 women and 7 men]) train more than 8 hours each week. Results 18,5% (20 [6 women and 14 men]) think that ECG is enough for screening for cardiovascular diseases. 20% (4) of them are coaches. Only 69,5% (77) of all respondents know about treadmill test. And 41,6% (32/77) of them know about the necessary screening for arrhythmogenic condition. 13% (10/77) of them train more than 8 hours per week. And only 27,3% (21/77) of people who know about treadmill test, passed it themselves. Also 21,6% (24) of all respondents think that any episode of arrhythmia is the  contraindication for any sport. But 96,4% (107) of the respondents know that it is necessary to regularly screen the cardiovascular system, even in the absence of complaints. 9% (10) of the interviewed think that diet is not important for people with cardiovascular diseases. And 18,9% (21) of the respondents don’t know about the effect of electrolytes on the body and the work of the heart muscle. Only 53,2% (59 [21 women and 38 men]) of the respondents trust the doctors more than coaches or themselves. And this is one of the reasons why it is necessary to talk about the basic principles of sports cardiology not only to doctors. 8,1% (9) of the respondents have never heard about any cases of sudden death of an athlete during training or at competitions due to «heart problems». 63,6% (21) of the coaches would not train a person who has suffered a heart attack. 71,8% (56) of the sportsmen would like to return to training after a heart attack. Conclusions Despite the fact that most people prefer a sedentary lifestyle, high-intensity fitness and long-distance endurance sport is getting more popular. Our survey proves the relatively low level of education about the underlying health conditions and possible risks associated with sports not only among  athletes but  professional coaches as well. There are no conflicts of interest to declare.

scholarly journals A Machine Vision Approach for Bioreactor Foam Sensing

2021 ◽  
pp. 247263032110088
Author(s):  
Jonas Austerjost ◽  
Robert Söldner ◽  
Christoffer Edlund ◽  
Johan Trygg ◽  
David Pollard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
State Of The Art ◽  
Low Cost ◽  
High Accuracy ◽  
Consumer Electronics ◽  
Learning System ◽  
Automotive Applications ◽  
Fine Grained

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

scholarly journals Deep Learning for the Classification of Non-Hodgkin Lymphoma on Histopathological Images

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2419
Author(s):  
Georg Steinbuss ◽  
Mark Kriegsmann ◽  
Christiane Zgorzelski ◽  
Alexander Brobeil ◽  
Benjamin Goeppert ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hodgkin Lymphoma ◽  
Expert Knowledge ◽  
B Cell Lymphoma ◽  
High Accuracy ◽  
Lymphocytic Leukemia ◽  
Non Hodgkin Lymphoma ◽  
Image Patches ◽  
Histopathological Images

The diagnosis and the subtyping of non-Hodgkin lymphoma (NHL) are challenging and require expert knowledge, great experience, thorough morphological analysis, and often additional expensive immunohistological and molecular methods. As these requirements are not always available, supplemental methods supporting morphological-based decision making and potentially entity subtyping are required. Deep learning methods have been shown to classify histopathological images with high accuracy, but data on NHL subtyping are limited. After annotation of histopathological whole-slide images and image patch extraction, we trained and optimized an EfficientNet convolutional neuronal network algorithm on 84,139 image patches from 629 patients and evaluated its potential to classify tumor-free reference lymph nodes, nodal small lymphocytic lymphoma/chronic lymphocytic leukemia, and nodal diffuse large B-cell lymphoma. The optimized algorithm achieved an accuracy of 95.56% on an independent test set including 16,960 image patches from 125 patients after the application of quality controls. Automatic classification of NHL is possible with high accuracy using deep learning on histopathological images and routine diagnostic applications should be pursued.

ALLERGENIC ACTIVITY AND DANGER OF INDUSTRIAL DUSTS OF DRY PRODUCTS OF COW'S MILK PROCESSING

2021 ◽  
pp. 51-55
Author(s):  
Baranov S.A. ◽  
◽  
Shevlyakov V.V. ◽  
Sychyk S.I. ◽  
Filonyuk V.A. ◽  
...  
Keyword(s):  
Milk Proteins ◽  
The Body ◽  
Cow’S Milk ◽  
Antigenic Determinants ◽  
Allergic Reactions ◽  
Cow's Milk ◽  
Milk Processing ◽  
Allergenic Activity

The purpose of the work was to establish in a model experiment the allergenic activity and danger of the extracts obtained from the dust of dry products of cow's milk processing (DPMP), containing complexes of soluble whey (WMP) or casein milk proteins (CMP), as a stage of hygienic regulation of the content of dust DPMP in the air of the working area. Experiments on albino guinea pigs sensitized by the intradermal injection of standard doses of WMP and СМР solutions into the ear revealed the development of severe allergic reactions in the animals of the experimental groups with the prevalence of mixed mechanisms of immediate anaphylactic and delayed cell-mediated types. According to the criteria for the classification of industrial allergens, the WMP and СМР complexes have a strong allergenic activity and are differentiated to the 1-st class of allergenic hazard, which determines the classification of the DPMP dust containing them as extremely dangerous industrial allergens. This is confirmed by the established high levels of indicators of allergic-diagnostic reactions in vivo and in vitro when testing sensitized WMP and СМР animals with a solution of skim milk powder dust, indicating the presence of antigenic determinants of whey and casein milk proteins in it and a real ability to form cross-allergic reactions in the body of workers to dust from all dry milk processing products containing these proteins.

Classification of diseases of the gastrointestinal tract in the practice of a family doctor

2021 ◽  
pp. 8-13
Author(s):  
Aleksey Borisovich Petrukhin
Keyword(s):  
Gastrointestinal Tract ◽  
Digestive System ◽  
Family Doctor ◽  
The Body ◽  
Internal Organs ◽  
Important Place ◽  
High Prevalence ◽  
Classification Of Diseases ◽  
Diagnosis Of Diseases

Gastroenterology belongs to one of the leading branches of therapy. In the structure of diseases of the internal organs, diseases of the digestive system occupy a particularly important place due to their high prevalence, which increases with age. As a rule, these diseases have a chronic, progressive, recurrent course, which ultimately leads to severe disorders of the activity of many organs and systems of the body. The article presents the basic requirements for the formation of a clinical diagnosis of diseases of the gastrointestinal tract, which are most common in the practice of a family doctor.

Analysing Temporal Effects on Classification of SAR and Optical Images

2021 ◽  
Author(s):  
Ahmet Batuhan Polat ◽  
Ozgun Akcay ◽  
Fusun Balik Sanli
Keyword(s):  
Rural Areas ◽  
Classification Accuracy ◽  
Winter Season ◽  
Support Vector ◽  
Classification Algorithms ◽  
Sar Images ◽  
Optical Images ◽  
Object Based ◽  
Training Samples

<p>Obtaining high accuracy in land cover classification is a non-trivial problem in geosciences for monitoring urban and rural areas. In this study, different classification algorithms were tested with different types of data, and besides the effects of seasonal changes on these classification algorithms and the evaluation of the data used are investigated. In addition, the effect of increasing classification training samples on classification accuracy has been revealed as a result of the study. Sentinel-1 Synthetic Aperture Radar (SAR) images and Sentinel-2 multispectral optical images were used as datasets. Object-based approach was used for the classification of various fused image combinations. The classification algorithms Support Vector Machines (SVM), Random Forest (RF) and K-Nearest Neighborhood (kNN) methods were used for this process. In addition, Normalized Difference Vegetation Index (NDVI) was examined separately to define the exact contribution to the classification accuracy.  As a result, the overall accuracies were compared by classifying the fused data generated by combining optical and SAR images. It has been determined that the increase in the number of training samples improve the classification accuracy. Moreover, it was determined that the object-based classification obtained from single SAR imagery produced the lowest classification accuracy among the used different dataset combinations in this study. In addition, it has been shown that NDVI data does not increase the accuracy of the classification in the winter season as the trees shed their leaves due to climate conditions.</p>

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