Research on Diabetes Risk Prediction Model at Early Stage Based on Machine Learning

2021 ◽  
pp. 613-619
Author(s):  
Yangyang Cui ◽  
Hankun Zhang ◽  
Song Wang ◽  
Zhenhua Liao ◽  
Weiqiang Liu
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Risk Prediction ◽  
Early Stage ◽  
Diabetes Risk ◽  
Risk Prediction Model

Diabetes risk prediction model for non-obese Asian Indians residing in North India using cut-off values for pancreatic and intra-abdominal fat volume and liver span

2016 ◽  
Vol 8 (5) ◽  
pp. 729-731 ◽  
Author(s):  
Shajith Anoop ◽  
Anoop Misra ◽  
Kalaivani Mani ◽  
Ravindra Mohan Pandey ◽  
Seema Gulati
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Asian Indians ◽  
Abdominal Fat ◽  
Diabetes Risk ◽  
Risk Prediction Model ◽  
North India ◽  
Fat Volume

scholarly journals A comparison of machine learning classifiers for dementia with Lewy bodies using miRNA expression data

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Daichi Shigemizu ◽  
Shintaro Akiyama ◽  
Yuya Asanomi ◽  
Keith A. Boroevich ◽  
Alok Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Prediction Model ◽  
Risk Prediction ◽  
Mirna Expression ◽  
Dementia With Lewy Bodies ◽  
Target Genes ◽  
Lewy Bodies ◽  
Risk Prediction Model ◽  
Gradient Boosting

Abstract Background Dementia with Lewy bodies (DLB) is the second most common subtype of neurodegenerative dementia in humans following Alzheimer’s disease (AD). Present clinical diagnosis of DLB has high specificity and low sensitivity and finding potential biomarkers of prodromal DLB is still challenging. MicroRNAs (miRNAs) have recently received a lot of attention as a source of novel biomarkers. Methods In this study, using serum miRNA expression of 478 Japanese individuals, we investigated potential miRNA biomarkers and constructed an optimal risk prediction model based on several machine learning methods: penalized regression, random forest, support vector machine, and gradient boosting decision tree. Results The final risk prediction model, constructed via a gradient boosting decision tree using 180 miRNAs and two clinical features, achieved an accuracy of 0.829 on an independent test set. We further predicted candidate target genes from the miRNAs. Gene set enrichment analysis of the miRNA target genes revealed 6 functional genes included in the DHA signaling pathway associated with DLB pathology. Two of them were further supported by gene-based association studies using a large number of single nucleotide polymorphism markers (BCL2L1: P = 0.012, PIK3R2: P = 0.021). Conclusions Our proposed prediction model provides an effective tool for DLB classification. Also, a gene-based association test of rare variants revealed that BCL2L1 and PIK3R2 were statistically significantly associated with DLB.

scholarly journals Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation

10.2196/23128 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e23128
Author(s):  
Pan Pan ◽  
Yichao Li ◽  
Yongjiu Xiao ◽  
Bingchao Han ◽  
Longxiang Su ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Data Set ◽  
Icu Patients ◽  
Potential Risk Factors

Background Patients with COVID-19 in the intensive care unit (ICU) have a high mortality rate, and methods to assess patients’ prognosis early and administer precise treatment are of great significance. Objective The aim of this study was to use machine learning to construct a model for the analysis of risk factors and prediction of mortality among ICU patients with COVID-19. Methods In this study, 123 patients with COVID-19 in the ICU of Vulcan Hill Hospital were retrospectively selected from the database, and the data were randomly divided into a training data set (n=98) and test data set (n=25) with a 4:1 ratio. Significance tests, correlation analysis, and factor analysis were used to screen 100 potential risk factors individually. Conventional logistic regression methods and four machine learning algorithms were used to construct the risk prediction model for the prognosis of patients with COVID-19 in the ICU. The performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC). Interpretation and evaluation of the risk prediction model were performed using calibration curves, SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), etc, to ensure its stability and reliability. The outcome was based on the ICU deaths recorded from the database. Results Layer-by-layer screening of 100 potential risk factors finally revealed 8 important risk factors that were included in the risk prediction model: lymphocyte percentage, prothrombin time, lactate dehydrogenase, total bilirubin, eosinophil percentage, creatinine, neutrophil percentage, and albumin level. Finally, an eXtreme Gradient Boosting (XGBoost) model established with the 8 important risk factors showed the best recognition ability in the training set of 5-fold cross validation (AUC=0.86) and the verification queue (AUC=0.92). The calibration curve showed that the risk predicted by the model was in good agreement with the actual risk. In addition, using the SHAP and LIME algorithms, feature interpretation and sample prediction interpretation algorithms of the XGBoost black box model were implemented. Additionally, the model was translated into a web-based risk calculator that is freely available for public usage. Conclusions The 8-factor XGBoost model predicts risk of death in ICU patients with COVID-19 well; it initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients.

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