scholarly journals Impact of machine learning and feature selection on type 2 diabetes risk prediction

2020 ◽  
Vol 3 ◽  
pp. 10-10 ◽  
Author(s):  
Päivi Riihimaa
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Feature Selection ◽  
Risk Prediction ◽  
Diabetes Risk

scholarly journals Machine Learning Tools for Long-term Type 2 Diabetes Risk Prediction

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Nikos Fazakis ◽  
Otilia Kocsis ◽  
Elias Dritsas ◽  
Sotiris Alexiou ◽  
Nikos Fakotakis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Risk Prediction ◽  
Diabetes Risk ◽  
Learning Tools ◽  
Term Type

scholarly journals Predicting the Development of Type 2 Diabetes in a Large Australian Cohort Using Machine-Learning Techniques: Longitudinal Survey Study (Preprint)

2019 ◽  
Author(s):  
Lei Zhang ◽  
Xianwen Shang ◽  
Subhashaan Sreedharan ◽  
Xixi Yan ◽  
Jianbin Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Risk Prediction ◽  
Machine Learning Algorithms ◽  
Survey Study ◽  
Gradient Boosting ◽  
Diabetes Incidence ◽  
Diabetes Onset ◽  
Prediction Of Diabetes

BACKGROUND Previous conventional models for the prediction of diabetes could be updated by incorporating the increasing amount of health data available and new risk prediction methodology. OBJECTIVE We aimed to develop a substantially improved diabetes risk prediction model using sophisticated machine-learning algorithms based on a large retrospective population cohort of over 230,000 people who were enrolled in the study during 2006-2017. METHODS We collected demographic, medical, behavioral, and incidence data for type 2 diabetes mellitus (T2DM) in over 236,684 diabetes-free participants recruited from the 45 and Up Study. We predicted and compared the risk of diabetes onset in these participants at 3, 5, 7, and 10 years based on three machine-learning approaches and the conventional regression model. RESULTS Overall, 6.05% (14,313/236,684) of the participants developed T2DM during an average 8.8-year follow-up period. The 10-year diabetes incidence in men was 8.30% (8.08%-8.49%), which was significantly higher (odds ratio 1.37, 95% CI 1.32-1.41) than that in women at 6.20% (6.00%-6.40%). The incidence of T2DM was doubled in individuals with obesity (men: 17.78% [17.05%-18.43%]; women: 14.59% [13.99%-15.17%]) compared with that of nonobese individuals. The gradient boosting machine model showed the best performance among the four models (area under the curve of 79% in 3-year prediction and 75% in 10-year prediction). All machine-learning models predicted BMI as the most significant factor contributing to diabetes onset, which explained 12%-50% of the variance in the prediction of diabetes. The model predicted that if BMI in obese and overweight participants could be hypothetically reduced to a healthy range, the 10-year probability of diabetes onset would be significantly reduced from 8.3% to 2.8% (<i>P</i>&lt;.001). CONCLUSIONS A one-time self-reported survey can accurately predict the risk of diabetes using a machine-learning approach. Achieving a healthy BMI can significantly reduce the risk of developing T2DM.

scholarly journals Factors Motivating Individuals to Consider Genetic Testing for Type 2 Diabetes Risk Prediction

PLoS ONE ◽  
2016 ◽  
Vol 11 (1) ◽  
pp. e0147071 ◽  
Author(s):  
Jennifer Wessel ◽  
Jyoti Gupta ◽  
Mary de Groot
Keyword(s):  
Type 2 Diabetes ◽  
Genetic Testing ◽  
Risk Prediction ◽  
Diabetes Risk

scholarly journals A Decade of Genetic and Metabolomic Contributions to Type 2 Diabetes Risk Prediction

2017 ◽  
Vol 17 (12) ◽  
Author(s):  
Jordi Merino ◽  
Miriam S. Udler ◽  
Aaron Leong ◽  
James B. Meigs
Keyword(s):  
Type 2 Diabetes ◽  
Risk Prediction ◽  
Diabetes Risk

scholarly journals Interpretable Machine Learning Framework Reveals Robust Gut Microbiome Features Associated With Type 2 Diabetes

2020 ◽  
Author(s):  
Wanglong Gou ◽  
Chu-wen Ling ◽  
Yan He ◽  
Zengliang Jiang ◽  
Yuanqing Fu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Gut Microbiome ◽  
Diabetes Risk ◽  
Dietary Factors ◽  
Blood Metabolites ◽  
Learning Framework ◽  
Interpretable Machine Learning ◽  
Prospective Association

<b>OBJECTIVE </b>To identify the core gut microbial features associated with type 2 diabetes risk, and potential demographic, adiposity and dietary factors associated with these features.<b></b> <p><b>RESEARCH DESIGN AND METHODS </b><a>We used an interpretable machine learning framework to identify the type 2 diabetes-related </a>gut microbiome features in the cross-sectional analyses of three Chinese cohorts: <a></a><a>one discovery cohort </a>(n=1832, 270 cases) and two validation cohorts (cohort 1: n=203, 48 cases; cohort 2: n=7009, 608 cases). We constructed a microbiome risk score (MRS) with the identified features. We examined the prospective association of the MRS with glucose increment in 249 non-T2D participants, and assessed the correlation between the MRS and host blood metabolites (n=1016). We transferred human faecal samples with different MRS levels to <a>germ-free mice </a>to confirm the <a>MRS-</a>type 2 diabetes relationship. We then examined the prospective association of demographic, adiposity and dietary factors with the MRS (n=1832).<b></b></p> <p><b>RESULTS<a> </a></b><a></a><a>The MRS (including 14 </a>microbial features) consistently associated with type 2 diabetes, with risk ratio for per one unit change in MRS 1.28 (95%CI 1.23-1.33), 1.23 (1.13-1.34) and 1.12 (1.06-1.18) across 3 cohorts. The MRS was positively associated with future glucose increment (P<0.05), and was correlated with a variety of gut microbiota-derived blood metabolites. Animal study further <a>confirms the MRS-</a>type 2 diabetes relationship. Body fat distribution was found to be a key factor modulating the gut microbiome-type 2 diabetes relationship. <b></b></p> <b>CONCLUSIONS </b>Our results reveal a core set of gut microbiome features associated with type 2 diabetes risk and future glucose increment.

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