scholarly journals Estimate of the HOMA-IR Cut-off Value Identifying Subjects at Risk of Insulin Resistance using a Machine Learning Approach

2021 ◽  
Author(s):  
Abdelhamid Abdessalem ◽  
Hamza Zidoum ◽  
Fahd Zadjali ◽  
Rachid Hedjam ◽  
Aliya Al-Ansari ◽  
...  
Keyword(s):  
Machine Learning ◽  
Insulin Resistance ◽  
At Risk ◽  
Clinical Data ◽  
Clustering Algorithm ◽  
Early Stage ◽  
Strong Relationship ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Research Findings

Objective: This paper describes an unsupervised Machine Learning approach to estimate the HOMA-IR cut-off identifying subjects at risk of insulin resistance in a given ethnic group, based on the clinical data of a representative sample. Methods: We apply the approach to clinical data of individuals of Arab ancestors obtained from a family study conducted in the city of Nizwa between January 2000 and December 2004. First, we identify HOMA-IR-correlated variables to which we apply our own clustering algorithm. Two clusters having the smallest overlap in their HOMA-IR values are returned. These clusters represent samples of two populations: insulin sensitive subjects and individuals at risk of insulin resistance. The cut-off value is estimated from intersections of the Gaussian functions modelling the HOMA-IR distributions of these populations. Results: We identified a HOMA-IR cut-off value of 1.62+/-0.06. We demonstrated the validity of this cut-off by 1) Showing that clinical characteristics of the identified groups match well published research findings about insulin resistance. 2) Showing a strong relationship between the segmentations resulting from the proposed cut-off and that resulting from the 2-hours glucose cut-off recommended by WHO for detecting prediabetes. Finally, we showed that the method is also able to identify cut-off values for similar problems (e.g. fasting sugar cut-off for prediabetes). Conclusion: The proposed method defines a HOMA-IR cut-off value for detecting individuals at risk of insulin resistance. Such method can identify high risk individuals at early stage which may prevent or at least delay the onset of chronic diseases like type 2 diabetes. Keywords: Machine Learning; Feature Selection; K-mean++ Clustering; Insulin Resistance; HOMA-IR; T2DM.

scholarly journals Role of Machine Learning Approach for Detection and Classification of Diseases in Cotton Plant

2021 ◽  
Vol 12 (5) ◽  
pp. 810-817
Author(s):  
Sandhya N. dhage, Dr. Vijay Kumar Garg
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Cotton Plant ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Learning Approach ◽  
Early Detection Of Disease ◽  
Cotton Crop ◽  
Machine Learning Approach ◽  
Crop Diseases

Qualitative and quantitative agricultural production leads to economic benefits which can be achieved by periodic monitoring of crop, detection and prevention of crop diseases and insects. Quality of crop production is reduced by pest infection and crop diseases. Existing measures involves manual detection of cotton diseases by farmers and experts which requires  regular monitoring and detection manifest at middle to later stage of infection which causes many disadvantages such as becoming  too late for diseases to be cured.  Lack of early detection of diseases causes the diseases to be spread in nearby crops in the field and also spraying of pesticides is done on entire field for minimizing the infection of disease. The main goal of proposed research topic is to find the solution to the agriculture problem which involves detecting disease in cotton plant at early stage and classify the disease based on symptoms. Early detection of disease at an early stage prevent it from spreading to another area and preventive measures can be taken by farmers by spraying pesticides to control its growth which helps to increase the cotton yield production. Automatic identification of the different diseases affecting cotton crop will give many benefits to the farmers so that time, money will be saved and also gives healthy life to the crop. The contribution of this paper is to present the machine learning approach used for cotton crop disease diagnosis and classification.

scholarly journals Control Oriented Prediction of Driver Brake Intention and Intensity Using a Composite Machine Learning Approach

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2483 ◽  
Author(s):  
Jianhao ◽  
Jing ◽  
Longqiang ◽  
Yi ◽  
Hanzhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
External Input ◽  
Feature Subset Selection ◽  
Learning Approach ◽  
Area Network ◽  
Feature Subset ◽  
Sensor Failure ◽  
Machine Learning Approach ◽  
And Control

Driver perception, decision, and control behaviors are easily affected by traffic conditions and driving style, showing the tendency of randomness and personalization. Brake intention and intensity are integrated and control-oriented parameters that are crucial to the development of an intelligent braking system. In this paper, a composite machine learning approach was proposed to predict driver brake intention and intensity with a proper prediction horizon. Various driving data were collected from Controller Area Network (CAN) bus under a real driving condition, which mainly contained urban and rural road types. ReliefF and RReliefF (they don’t have abbreviations) algorithms were employed as feature subset selection methods and applied in a prepossessing step before the training. The rank importance of selected predictors exhibited different trends or even negative trends when predicting brake intention and intensity. A soft clustering algorithm, Fuzzy C-means, was adopted to label the brake intention into categories, namely slight, medium, intensive, and emergency braking. Data sets with misplaced labels were used for training of an ensemble machine learning method, random forest. It was validated that brake intention could be accurately predicted 0.5 s ahead. An open-loop nonlinear autoregressive with external input (NARX) network was capable of learning the long-term dependencies in comparison to the static neural network and was suggested for online recognition and prediction of brake intensity 1 s in advance. As system redundancy and fault tolerance, a close-loop NARX network could be adopted for brake intensity prediction in the case of possible sensor failure and loss of CAN message.

scholarly journals Adding Continuous Vital Sign Information to Static Clinical Data Improves the Prediction of Length of Stay After Intubation: A Data-Driven Machine Learning Approach

2020 ◽  
Vol 65 (9) ◽  
pp. 1367-1377
Author(s):  
David Castiñeira ◽  
Katherine R Schlosser ◽  
Alon Geva ◽  
Amir R Rahmani ◽  
Gaston Fiore ◽  
...  
Keyword(s):  
Machine Learning ◽  
Length Of Stay ◽  
Clinical Data ◽  
Vital Sign ◽  
Data Driven ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Prediction of treatment outcome in burning mouth syndrome patients using machine learning based on clinical data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Moon-Jong Kim ◽  
Pil-Jong Kim ◽  
Hong-Gee Kim ◽  
Hong-Seop Kho
Keyword(s):  
Machine Learning ◽  
Treatment Outcomes ◽  
Clinical Data ◽  
Prediction Models ◽  
Burning Mouth Syndrome ◽  
Learning Approach ◽  
Initial Approach ◽  
Machine Learning Approach ◽  
Burning Mouth ◽  
Clonazepam Therapy

AbstractThe purpose of this study is to apply a machine learning approach to predict whether patients with burning mouth syndrome (BMS) respond to the initial approach and clonazepam therapy based on clinical data. Among the patients with the primary type of BMS who visited the clinic from 2006 to 2015, those treated with the initial approach of detailed explanation regarding home care instruction and use of oral topical lubricants, or who were prescribed clonazepam for a minimum of 1 month were included in this study. The clinical data and treatment outcomes were collected from medical records. Extreme Gradient-Boosted Decision Trees was used for machine learning algorithms to construct prediction models. Accuracy of the prediction models was evaluated and feature importance calculated. The accuracy of the prediction models for the initial approach and clonazepam therapy was 67.6% and 67.4%, respectively. Aggravating factors and psychological distress were important features in the prediction model for the initial approach, and intensity of symptoms before administration was the important feature in the prediction model for clonazepam therapy. In conclusion, the analysis of treatment outcomes in patients with BMS using a machine learning approach showed meaningful results of clinical applicability.

An Innovative Machine Learning Approach to Diagnose Cancer at an Early Stage

2021 ◽  
pp. 313-337
Author(s):  
P. Poongodi ◽  
E. Udayakumar ◽  
K. Srihari ◽  
Nandan Mohanty Sachi
Keyword(s):  
Machine Learning ◽  
Early Stage ◽  
Learning Approach ◽  
Machine Learning Approach
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