scholarly journals Prediction of inherited genomic susceptibility to 20 common cancer types by a supervised machine-learning method

2018 ◽  
Vol 115 (6) ◽  
pp. 1322-1327 ◽  
Author(s):  
Byung-Ju Kim ◽  
Sung-Hou Kim
Keyword(s):  
Machine Learning ◽  
Early Intervention ◽  
Lifestyle Factors ◽  
Supervised Machine Learning ◽  
Cancer Type ◽  
Genome Wide Association Studies ◽  
Machine Learning Method ◽  
Learning Method ◽  
Common Cancer ◽  
Cancer Types

Prevention and early intervention are the most effective ways of avoiding or minimizing psychological, physical, and financial suffering from cancer. However, such proactive action requires the ability to predict the individual’s susceptibility to cancer with a measure of probability. Of the triad of cancer-causing factors (inherited genomic susceptibility, environmental factors, and lifestyle factors), the inherited genomic component may be derivable from the recent public availability of a large body of whole-genome variation data. However, genome-wide association studies have so far showed limited success in predicting the inherited susceptibility to common cancers. We present here a multiple classification approach for predicting individuals’ inherited genomic susceptibility to acquire the most likely phenotype among a panel of 20 major common cancer types plus 1 “healthy” type by application of a supervised machine-learning method under competing conditions among the cohorts of the 21 types. This approach suggests that, depending on the phenotypes of 5,919 individuals of “white” ethnic population in this study, (i) the portion of the cohort of a cancer type who acquired the observed type due to mostly inherited genomic susceptibility factors ranges from about 33 to 88% (or its corollary: the portion due to mostly environmental and lifestyle factors ranges from 12 to 67%), and (ii) on an individual level, the method also predicts individuals’ inherited genomic susceptibility to acquire the other types ranked with associated probabilities. These probabilities may provide practical information for individuals, heath professionals, and health policymakers related to prevention and/or early intervention of cancer.

scholarly journals Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning

2018 ◽  
Vol 11 (4) ◽  
pp. 70 ◽  
Author(s):  
Jung-sik Hong ◽  
Hyeongyu Yeo ◽  
Nam-Wook Cho ◽  
Taeuk Ahn
Keyword(s):  
Machine Learning ◽  
Random Forests ◽  
Area Under The Curve ◽  
High Accuracy ◽  
Supervised Machine Learning ◽  
Machine Learning Method ◽  
Learning Method ◽  
Machine Learning Technique ◽  
Novel Approach ◽  
Learning Technique

Since not all suppliers are to be managed in the same way, a purchasing strategy requires proper supplier segmentation so that the most suitable strategies can be used for different segments. Most existing methods for supplier segmentation, however, either depend on subjective judgements or require significant efforts. To overcome the limitations, this paper proposes a novel approach for supplier segmentation. The objective of this paper is to develop an automated and effective way to identify core suppliers, whose profit impact on a buyer is significant. To achieve this objective, the application of a supervised machine learning technique, Random Forests (RF), to e-invoice data is proposed. To validate the effectiveness, the proposed method has been applied to real e-invoice data obtained from an automobile parts manufacturer. Results of high accuracy and the area under the curve (AUC) attest to the applicability of our approach. Our method is envisioned to be of value for automating the identification of core suppliers. The main benefits of the proposed approach include the enhanced efficiency of supplier segmentation procedures. Besides, by utilizing a machine learning method to e-invoice data, our method results in more reliable segmentation in terms of selecting and weighting variables.

scholarly journals Single Image De-Raining using Supervised CNN Model

2021 ◽  
Vol 9 (10) ◽  
pp. 244-250
Author(s):  
Dr. Geeta Hanji
Keyword(s):  
Machine Learning ◽  
High Frequency ◽  
Accurate Result ◽  
Supervised Machine Learning ◽  
Machine Learning Method ◽  
Learning Method ◽  
Single Image ◽  
Elapsed Time ◽  
Difficult Issue

Abstract: An image captured in rain reduces the visibility quality of image which affects the analytical task like detecting objects and classifying pictures. Hence, image de-raining became important in last few years. Since pictures taken in rain include rain streaks of all sizes, single image de-raining is becoming much difficult issue to solve, which may flow in different direction and the density of each rain streak is different. Rain streaks have a varied effect on various areas of picture, and hence it becomes important for removing rain streak from rainy pictures as rainy images tend to lose its high frequency information; previously many methods were proposed for this purpose but they failed to provide accurate results. Hence we have studied and implemented a supervised machine learning method using convolutional neural network (CNN) algorithm to get more accurate result of rain streak removal from an image captured during rain and in less elapsed time by preserving high rated information of image during removal of rain streak. Keywords: CNN, elapsed time, single image de-raining, supervised machine learning, rain streaks.

scholarly journals Prognostic Value of Multiple Circulating Biomarkers for 2-Year Death in Acute Heart Failure With Preserved Ejection Fraction

2021 ◽  
Vol 8 ◽  
Author(s):  
Yan Gao ◽  
Xueke Bai ◽  
Jiapeng Lu ◽  
Lihua Zhang ◽  
Xiaofang Yan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Ejection Fraction ◽  
Risk Stratification ◽  
Supervised Machine Learning ◽  
Machine Learning Method ◽  
Learning Method ◽  
Preserved Ejection Fraction ◽  
Circulating Biomarkers ◽  
Svm Model

Background: Heart failure with preserved ejection fraction (HFpEF) is increasingly recognized as a major global public health burden and lacks effective risk stratification. We aimed to assess a multi-biomarker model in improving risk prediction in HFpEF.Methods: We analyzed 18 biomarkers from the main pathophysiological domains of HF in 380 patients hospitalized for HFpEF from a prospective cohort. The association between these biomarkers and 2-year risk of all-cause death was assessed by Cox proportional hazards model. Support vector machine (SVM), a supervised machine learning method, was used to develop a prediction model of 2-year all-cause and cardiovascular death using a combination of 18 biomarkers and clinical indicators. The improvement of this model was evaluated by c-statistics, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).Results: The median age of patients was 71-years, and 50.5% were female. Multiple biomarkers independently predicted the 2-year risk of death in Cox regression model, including N-terminal pro B-type brain-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T (hs-TnT), growth differentiation factor-15 (GDF-15), tumor necrosis factor-α (TNFα), endoglin, and 3 biomarkers of extracellular matrix turnover [tissue inhibitor of metalloproteinases (TIMP)-1, matrix metalloproteinase (MMP)-2, and MMP-9) (FDR < 0.05). The SVM model effectively predicted the 2-year risk of all-cause death in patients with acute HFpEF in training set (AUC 0.834, 95% CI: 0.771–0.895) and validation set (AUC 0.798, 95% CI: 0.719–0.877). The NRI and IDI indicated that the SVM model significantly improved patient classification compared to the reference model in both sets (p < 0.05).Conclusions: Multiple circulating biomarkers coupled with an appropriate machine-learning method could effectively predict the risk of long-term mortality in patients with acute HFpEF. It is a promising strategy for improving risk stratification in HFpEF.

scholarly journals Modeling Commuter’s Sociodemographic Characteristics to Predict Public Transport Usage Frequency by Applying Supervised Machine Learning Method

2019 ◽  
Vol 15 (2) ◽  
pp. 1-7
Author(s):  
Nabeel Shakeel ◽  
Farrukh Baig ◽  
Muhammad Abubakar Saddiq
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Survey Data ◽  
Public Transport ◽  
Learning Algorithm ◽  
Supervised Machine Learning ◽  
Machine Learning Method ◽  
Learning Method ◽  
Transport Services ◽  
Usage Frequency

Abstract Predictive modeling is the key fundamental method to study passengers’ behavior in transportation research. One of the limited studied topic is modeling of public transport usage frequency, which can be used to estimate present and future demand and users’ trend toward public transport services. The artificial intelligence and machine learning methods are promising to be better substitute to statistical techniques. No doubt, traditionally been used econometrics models are better for causal relationship studies among variables, but they made rigid assumptions and unable to recognize the pattern in data. This paper aims to build a predictive model to solve passengers’ classification, and public transport usage frequency using socio-demographic survey data. The supervised machine learning algorithm, K-Nearest Neighbor (KNN) applied to build a predictive model, which is the better machine learning method for dealing with small datasets, because of its ability of having less parameter tuning. Survey data has been used to train and validate the model performance, which is able to predict public transport usage frequency of future users of public transport. This model can practically be used by public transport agencies and relevant government organizations to predict the public transport demand for new commuters before introducing any new transportation projects.

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