Development and validation of various phenotyping algorithms for Diabetes Mellitus using data from electronic health records

Esteban, Santiago; Tablado, Manuel Rodrguez; Peper, Ferdinand; Mahumud, Yamila S.; Ricci, Ricardo Ignacio; Kopitowski, Karin; Terrasa, Sergio

doi:10.1016/j.cmpb.2017.09.009

Cited by 20 publications

(21 citation statements)

References 49 publications

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“…Furthermore, the model was implemented by building a web-based system for diagnosing diabetes. [25] presented a comparison of ML model using a private dataset from EHR at Hospital Italiano de Buenos Aires, Buenos Aires, Argentina. The stacked generalization meta-learner was the greatest model compared with another model through Kappa coefficient value of 0.95 (95% CI 0.91, 0.98).…”

Section: Review Machine Learning Methods For Diabetes Predictionmentioning

confidence: 99%

Machine Learning Methods for Diabetes Prediction

Dzakiyullah¹,

Burhanuddin²,

Ikram³

et al. 2019

IJITEE

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Machine Learning is one of the methods used for task prediction. In the diabetic’s research field, the application of machine learning is emerging since the advantages of approximation on the prediction technique has significantly given insight for many health practitioners. Machine Learning is utilized in order to handle the uncontrollable risk factor by finding a relation between such a risk factor trough prediction. This study aims to review recent machine learning models that have been used in diabetes prediction with respect to the risk factors in order to prevent diabetes. This study compares the performance of the model by justified the accuracy as the baseline to evaluate the model. The result of this review shows that the Random Forest and Support Vector Machine are the most popular technique among researcher. Moreover, from this study, it can be seen that Type 2 Diabetes Mellitus (T2DM) has been a concern by researchers since the incidence of diabetes was increasing in worldwide today that happened from an uncontrollable risk factor

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Section: Review Machine Learning Methods For Diabetes Predictionmentioning

confidence: 99%

Machine Learning Methods for Diabetes Prediction

Dzakiyullah¹,

Burhanuddin²,

Ikram³

et al. 2019

IJITEE

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“…Several types of machine-learning algorithms, including instance-based ( Esteban et al, 2017 ; Kagawa et al, 2017 ; Nilashi et al, 2017 ; Pei et al, 2019 ; Talaei-Khoei & Wilson, 2018 ), decision trees ( Alghamdi et al, 2017 ; Esteban et al, 2017 ; Pei et al, 2019 ; Talaei-Khoei & Wilson, 2018 ), artificial neural network ( Esteban et al, 2017 ; Nilashi et al, 2017 ; Talaei-Khoei & Wilson, 2018 ), ensemble ( Alghamdi et al, 2017 ; Esteban et al, 2017 ; Pei et al, 2019 ), Bayesian ( Alghamdi et al, 2017 ; Anderson et al, 2015 ; Esteban et al, 2017 ; Maniruzzaman et al, 2017 ; Pei et al, 2019 ), statistical model ( Alghamdi et al, 2017 ; Esteban et al, 2017 ; Maniruzzaman et al, 2017 ; Talaei-Khoei & Wilson, 2018 ; Wu et al, 2018 ), and others (see Table 1 ), have been adopted to predict T2DM-related issues. However, these studies revealed different results in predicting the onset of T2DM even with the same machine-learning algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…For example, Pei et al (2019) and Alghamdi et al (2017) both adopted J48 as one of their algorithms for predicting the onset of T2DM, only Pei et al (2019) found that J48 had the best performance. The performance of support vector machine also differs among opposing studies ( Esteban et al, 2017 ; Kagawa et al, 2017 ; Nilashi et al, 2017 ; Pei et al, 2019 ; Talaei-Khoei & Wilson, 2018 ). Further, not all inclusionary studies adopted the same algorithms, making it difficult to accurately compare the performance of differing algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…While previous DM prediction studies have shown a potential for detecting the onset of T2DM ( Alghamdi et al, 2017 ; Anderson et al, 2015 ; Esteban et al, 2017 ; Kagawa et al, 2017 ; Maniruzzaman et al, 2017 ; Nilashi et al, 2017 ; Pei et al, 2019 ; Talaei-Khoei & Wilson, 2018 ; Upadhyaya et al, 2017 ; Wu et al, 2018 ), no studies, to our knowledge, have been aimed at predicting a suitable diagnosis code for T2DM patients. Further, ensemble machine-learning techniques such as bagging and boosting approaches are less utilized in these studies ( Esteban et al, 2017 ). Most importantly, less multi-class studies, to our knowledge, have been conducted ( Esteban et al, 2017 ).…”

Section: Introductionmentioning

confidence: 98%

“…Further, ensemble machine-learning techniques such as bagging and boosting approaches are less utilized in these studies ( Esteban et al, 2017 ). Most importantly, less multi-class studies, to our knowledge, have been conducted ( Esteban et al, 2017 ). Therefore, the intended purpose of this study is to leverage routinely available clinical data in order to establish a multi-class predictive model based on bagging and boosting machine-learning techniques useful to identify Asian T2DM patients with a corresponding diagnosis code.…”

Section: Introductionmentioning

confidence: 99%

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A multi-class classification model for supporting the diagnosis of type II diabetes mellitus

Kuo

Talley

Kao

et al. 2020

PeerJ

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Background Numerous studies have utilized machine-learning techniques to predict the early onset of type 2 diabetes mellitus. However, fewer studies have been conducted to predict an appropriate diagnosis code for the type 2 diabetes mellitus condition. Further, ensemble techniques such as bagging and boosting have likewise been utilized to an even lesser extent. The present study aims to identify appropriate diagnosis codes for type 2 diabetes mellitus patients by means of building a multi-class prediction model which is both parsimonious and possessing minimum features. In addition, the importance of features for predicting diagnose code is provided. Methods This study included 149 patients who have contracted type 2 diabetes mellitus. The sample was collected from a large hospital in Taiwan from November, 2017 to May, 2018. Machine learning algorithms including instance-based, decision trees, deep neural network, and ensemble algorithms were all used to build the predictive models utilized in this study. Average accuracy, area under receiver operating characteristic curve, Matthew correlation coefficient, macro-precision, recall, weighted average of precision and recall, and model process time were subsequently used to assess the performance of the built models. Information gain and gain ratio were used in order to demonstrate feature importance. Results The results showed that most algorithms, except for deep neural network, performed well in terms of all performance indices regardless of either the training or testing dataset that were used. Ten features and their importance to determine the diagnosis code of type 2 diabetes mellitus were identified. Our proposed predictive model can be further developed into a clinical diagnosis support system or integrated into existing healthcare information systems. Both methods of application can effectively support physicians whenever they are diagnosing type 2 diabetes mellitus patients in order to foster better patient-care planning.

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Cardiac Arrhythmia Classification Using Machine Learning Techniques

Singh

2018

Lecture Notes in Electrical Engineering

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Development and validation of various phenotyping algorithms for Diabetes Mellitus using data from electronic health records

Cited by 20 publications

References 49 publications

Machine Learning Methods for Diabetes Prediction

Machine Learning Methods for Diabetes Prediction

A multi-class classification model for supporting the diagnosis of type II diabetes mellitus

Cardiac Arrhythmia Classification Using Machine Learning Techniques

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