Comparison of machine-learning algorithms to build a predictive model for detecting undiagnosed diabetes - ELSA-Brasil : accuracy study
André Rodrigues Olivera,Valter Roesler,Cirano Iochpe,Maria Inês Schmidt,Álvaro Vigo,Sandhi Maria Barreto,Bruce Bartholow Duncan +6 more
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TLDR
Most of the predictive models produced similar results, and demonstrated the feasibility of identifying individuals with highest probability of having undiagnosed diabetes, through easily-obtained clinical data.Abstract:
CONTEXT AND OBJECTIVE: Type 2 diabetes is a chronic disease associated with a wide range of serious health complications that have a major impact on overall health. The aims here were to develop and validate predictive models for detecting undiagnosed diabetes using data from the Longitudinal Study of Adult Health (ELSA-Brasil) and to compare the performance of different machine-learning algorithms in this task. DESIGN AND SETTING: Comparison of machine-learning algorithms to develop predictive models using data from ELSA-Brasil. METHODS: After selecting a subset of 27 candidate variables from the literature, models were built and validated in four sequential steps: (i) parameter tuning with tenfold cross-validation, repeated three times; (ii) automatic variable selection using forward selection, a wrapper strategy with four different machine-learning algorithms and tenfold cross-validation (repeated three times), to evaluate each subset of variables; (iii) error estimation of model parameters with tenfold cross-validation, repeated ten times; and (iv) generalization testing on an independent dataset. The models were created with the following machine-learning algorithms: logistic regression, artificial neural network, naive Bayes, K-nearest neighbor and random forest. RESULTS: The best models were created using artificial neural networks and logistic regression. These achieved mean areas under the curve of, respectively, 75.24% and 74.98% in the error estimation step and 74.17% and 74.41% in the generalization testing step. CONCLUSION: Most of the predictive models produced similar results, and demonstrated the feasibility of identifying individuals with highest probability of having undiagnosed diabetes, through easily-obtained clinical data.read more
Citations
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A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models
Evangelia Christodoulou,Jie Ma,Gary S. Collins,Ewout W. Steyerberg,Jan Y Verbakel,Ben Van Calster,Ben Van Calster +6 more
TL;DR: Improvements in methodology and reporting are needed for studies that compare modeling algorithms for clinical prediction modeling in the literature and found no evidence of superior performance of ML over LR.
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Early detection of type 2 diabetes mellitus using machine learning-based prediction models.
TL;DR: This study compares machine learning-based prediction models to commonly used regression models for prediction of undiagnosed T2DM and shows no clinically relevant improvement when more sophisticated prediction models were used.
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Current State of Diabetes Mellitus Prevalence, Awareness, Treatment, and Control in Latin America: Challenges and Innovative Solutions to Improve Health Outcomes Across the Continent
TL;DR: The prevalence of diabetes mellitus continues to rise across Latin America, and the number of those with the disease may be underestimated, however, some local governments are embedding more comprehensive diabetes assessments in their local national surveys.
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