Prediction and diagnosis of future diabetes risk: a machine learning approach

Birjais, Roshan; Mourya, Ashish Kumar; Chauhan, Ritu; Kaur, Harleen

doi:10.1007/s42452-019-1117-9

Cited by 81 publications

(27 citation statements)

References 19 publications

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“…Often applied are pattern recognition, disease prediction and classification using various data mining techniques 4 . Due to the increased prevalence of T2DM, various techniques have been used to build predictive models and models for early disease diagnosis, such as logistic and Cox proportional hazard regression models 5 – 7 , Random Forest 8 , 9 , boosted ensembles 10 , 11 , etc. The study by Damen et al 12 showed that logistic regression was used in most (n = 363) models for risk estimation in the general population.…”

Section: Introductionmentioning

confidence: 99%

Early detection of type 2 diabetes mellitus using machine learning-based prediction models

Kopitar

Kocbek

Cilar

et al. 2020

Sci Rep

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Most screening tests for T2DM in use today were developed using multivariate regression methods that are often further simplified to allow transformation into a scoring formula. The increasing volume of electronically collected data opened the opportunity to develop more complex, accurate prediction models that can be continuously updated using machine learning approaches. This study compares machine learning-based prediction models (i.e. Glmnet, RF, XGBoost, LightGBM) to commonly used regression models for prediction of undiagnosed T2DM. The performance in prediction of fasting plasma glucose level was measured using 100 bootstrap iterations in different subsets of data simulating new incoming data in 6-month batches. With 6 months of data available, simple regression model performed with the lowest average RMSE of 0.838, followed by RF (0.842), LightGBM (0.846), Glmnet (0.859) and XGBoost (0.881). When more data were added, Glmnet improved with the highest rate (+ 3.4%). The highest level of variable selection stability over time was observed with LightGBM models. Our results show no clinically relevant improvement when more sophisticated prediction models were used. Since higher stability of selected variables over time contributes to simpler interpretation of the models, interpretability and model calibration should also be considered in development of clinical prediction models. Type 2 diabetes mellitus (T2DM) is very common and is responsible for very considerable morbidity, mortality. Furthermore, it is a substantial financial drain both on individuals/families, health systems and societies. Of major concern is that the incidence and prevalence of T2DM are increasing rapidly-globally. In 2017, it was estimated that 425 million people had any type of diabetes (approx. 5.5% of worldwide population) of which 90% had T2DM and according to projection estimations the prevalence is going to increase substantially in the coming years; by 2,045, for example, a 48% increase of prevalence from the above numbers is expected or in absolute numbers an estimated 629 million people (approx. 6.6% of the worldwide population) are expected to be suffering from any type of diabetes 1. T2DM can lead to substantially increased risk of macrovascular and microvascular disease, especially in those with inadequate glycaemic control 2. Progression of T2DM from impaired fasting glucose is typically slow and more importantly, its symptoms may remain undetected for many years. Delays in diagnosis are an important contributory factor to poor control and risk of complications 3. Data mining is nowadays applied to various fields of science, including healthcare and medicine. Often applied are pattern recognition, disease prediction and classification using various data mining techniques 4. Due to the increased prevalence of T2DM, various techniques have been used to build predictive models and models for early disease diagnosis, such as logistic and Cox proportional hazard regression models 5-7 , Random Forest 8,9 , boosted ensembles 10,1...

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Section: Introductionmentioning

confidence: 99%

Early detection of type 2 diabetes mellitus using machine learning-based prediction models

Kopitar

Kocbek

Cilar

et al. 2020

Sci Rep

199

114

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“…In [33] they recommended a machine learning method for predicting and diagnosing possible diabetes threat We attempted to focus forward in this review on the onset of diabetes, and is one of the global 's highest degenerative illnesses, according to the Health Organization. We have attempted to show numerous approaches including certain Classification Algorithms like GB, LR, and NB, which can be used to diagnose diabetes disease with 86 %accuracy for Gradient Boosting, % accuracy for Logistic Regression, and % accuracy for Naive Bayes.…”

Section: In [5] They Have Explored Implementing Machinementioning

confidence: 99%

A detailed survey on Prognostication of diabetes diagnosis on the basis of machine learning techniques and the detection approaches to diabetic retinopathy using Artificial Intelligence

2021

IJATCSE

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The aim of the study is to compare, assess the optimum tools as well as the techniques and advanced features focused on prediction of diabetes diagnosis based on machine learning tactics and diabetic retinopathy using Artificial Intelligence. The literature on data science, Artificial Intelligence (AI) contains important knowledge and understanding of AI entities such as Data science, machine learning, deep learning, Medical image processing, feature extraction, classification techniques, etc. Diabetes diagnosis is a phenomenon that impacts individuals around the globe. Now, with diabetes impacting people from children to the elderly, the out-dated approaches to diabetes diagnosis should be replaced with new, time-saving technologies. There's several studies carried out by researchers to recognise and predict diabetes. Here plenty of classifiers in machine learning can be used, such as KNN, Random Tree, etc.They can save time and get more precise outcome when using these techniques to predict diabetes. Diabetic retinopathy (DR) is a typical disorder of diabetic disease that induces vision-impacting lesions in the retina. It also can turn to visual impairment if it is not addressed early. DR therapy only helps vision. Deep learning has in recent times being one of the most widely used approaches that has accomplished higher outcomes in so many fields, especially in the analysing and identification of medical image classification. In medical image processing, convolutional neural networks (CNN) using transfer learning are commonly used as a deep learning approach and they are incredibly beneficial. Key words: Diab

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“…Während derartige Modelle einfach zu trainieren und zu interpretieren sind, sind sie nicht in der Lage, komplexe nichtlineare Interaktionen der Variablen zu erfassen. In jüngster Zeit wurden anspruchsvollere maschinelle Lernalgorithmen wie "support vector machines", "random forests", "gradient boosting" und DNN in einer Reihe von Studien eingesetzt [32,34,35] und zeigten eine mögliche Verbesserung der Vorhersagen. Allerdings werden in diesen Modellen aufgrund vieler Vorteile, wie z.…”

Section: Risikovorhersage Und Diagnoseunclassified

Künstliche Intelligenz und maschinelles Lernen in der Diabetesforschung

et al. 2021

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In diesem Beitrag-Merkmale von Diabetes -Künstliche Intelligenz und maschinelles Lernen -Künstliche Intelligenz/maschinelles Lernen in der Diabetesforschung Risikovorhersage und Diagnose • Ergebnisvorhersage und Prognose • Mechanistisches Verständnis durch Omics-Studien in Menschen und Modellsystemen • Vorhersage von Diabeteskomplikationen -Herausforderungen und Zukunftsaussichten QR-Code scannen & Beitrag online lesen Zusammenfassung Hintergrund: Diabetes mellitus entwickelt sich zu einem globalen Gesundheitsproblem, das eine Transformation der Forschung und der medizinischen Praxis für ein besseres Patientenmanagement erfordert. Diesbezüglich bieten die Fülle an Daten und die Fortschritte in der Technologie und der künstlichen Intelligenz Möglichkeiten für ein solches Unterfangen. Ziele: Diese Übersichtsarbeit soll einen Überblick über künstliche Intelligenz und die aktuelle Forschung in ihrer Anwendung im Bereich Diabetes geben, insbesondere zur Risikovorhersage, Diagnose, Prognose und Vorhersage von Komplikationen. Fazit: Künstliche Intelligenz transformiert die Diabetesforschung in vielen technischen und organisatorischen Aspekten. Obwohl ihr Einsatz noch begrenzt und mit vielen Herausforderungen konfrontiert ist, wird sie wahrscheinlich künftig die medizinische Behandlung beeinflussen, indem sie eine automatisierte und personalisierte Gesundheitsversorgung für Erkrankte bietet. Schlüsselwörter Computergestützte Intelligenz • Algorithmen • Biostatistik • Computeranwendungen in der Biologie • Diabetes mellitus

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Prediction and diagnosis of future diabetes risk: a machine learning approach

Cited by 81 publications

References 19 publications

Early detection of type 2 diabetes mellitus using machine learning-based prediction models

Early detection of type 2 diabetes mellitus using machine learning-based prediction models

A detailed survey on Prognostication of diabetes diagnosis on the basis of machine learning techniques and the detection approaches to diabetic retinopathy using Artificial Intelligence

Künstliche Intelligenz und maschinelles Lernen in der Diabetesforschung

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