Survey on clinical prediction models for diabetes prediction

Jayanthi, N; Babu, B. Vijaya; Rao, N. Sambasiva

doi:10.1186/s40537-017-0082-7

Cited by 63 publications

(15 citation statements)

References 18 publications

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“…The most common tools used include Factor models, the Bayesian model and neural networks [5]. Besides, forecasting with big data that includes Time series methodology [6] will be beneficial for manufacturing [7], health care [8,9], and the retail sector [10]. Meanwhile, in the Tourism sector, big data forecasting plays a significant role [11][12][13].…”

Section: Related Studiesmentioning

confidence: 99%

Cyber risk prediction through social media big data analytics and statistical machine learning

Subroto

Apriyana²

2019

J Big Data

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Section: Related Studiesmentioning

confidence: 99%

Cyber risk prediction through social media big data analytics and statistical machine learning

Subroto

Apriyana²

2019

J Big Data

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“…Predictive analytics uses various methods like statistical or machine learning method to identify a prediction about future It consists of text mining for unstructured data, and give path to perform the next step. It uses both historical and present data to predict future regarding activity, behaviour and trends [1]. To do this it makes use of statistical analysis techniques, analytical queries and automated machine learning algorithms.…”

Section: Predicative Analysismentioning

confidence: 99%

“…It uses historical and present data to predict future regarding activity, behavior and trends. Supervised learning is a process of creating predictive models using a set of historical data and produce predictive results [1] …”

Section: Predicative Analysismentioning

confidence: 99%

“…Furthermore, in some instances, certain models can predict the possibility of having future disease or provide a prognosis for disease (e.g., complication or mortality). The aim of prediction modeling is to develop an accurate and useful clinical prediction model with multiple variables using comprehensive datasets [1]. Development of clinical prediction analysis include the following steps 1.…”

Section: Clinical Prediction Modelmentioning

confidence: 99%

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Prediction of diabetes with hybrid prediction model us-ing big data in health care

Kalaivani¹,

Marivendhan²,

Suma³

2017

IJET

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Technology is advancing in healthcare to comply with unique regulatory guidelines designed to support public safety. The realistic way of Big Data can unify all patient related data to get a 360-degree view of the patient to analyze and predict outcomes. Big Data is a buzzword which is reigning the innovation market from quiet sometime and trends which can give birth to new line of treatment of diseases and provide high quality healthcare at lower cost to all. These issues include benefits of Big Data, its applications and opportunities in medical areas and health care. This paper includes the basics of big data, clinical prediction model for predicting whether the diagnosed patient suffers from diabetes. Hybrid prediction model is chosen and it is used to predict the disease.

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“…It may also affect the lifestyle and eating pattern, all resulting in higher risk of developing type 2 diabetes. [7,8,9] Researchers have used traditional and hybrid approach as a predictive model and showed hybrid model to be more accurate than traditional model [11].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Risk of Type 2 Diabetes Mellitus with Stress as a Risk Factor using Classification Algorithms

Patil*,

Shah

2019

IJRTE

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Rising prevalence of type 2 diabetes mellitus is a vital health concern today, not only in India but across the world. Several factors including dietary habits, genetics, lack of physical exercise and stress are known to affect the risk of type 2 diabetes. Although awareness has increased to some extent, many people with diabetes have limited knowledge about the risk factors before the diagnosis of disease. For chronic disease prevention there is a necessity to find out such risk factors and manage them appropriately. Statistical techniques can be employed to understand the risk of type 2 diabetes in different age group of people. The objective of the research was to evaluate relationship among stress and type 2 diabetes in people with different age groups by a statistical tool. The proposed method uses three machine learning classifiers namely Support Vector Machine (SVM), Logistic Regression and Random Forest (RF) to detect type 2 diabetes at an early stage. To develop an adaptive model the preprocessing step has been applied. The accuracy of predicting diabetes using SVM, Random Forest and Logistic Regression was 80.17%, 79.37%, 78.67% respectively. The results suggest that as compared to Random Forest and Logistic Regression, SVM is better in predicting occurrence and progress of type 2 diabetes mellitus with stress as a risk factor.

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Survey on clinical prediction models for diabetes prediction

Cited by 63 publications

References 18 publications

Cyber risk prediction through social media big data analytics and statistical machine learning

Cyber risk prediction through social media big data analytics and statistical machine learning

Prediction of diabetes with hybrid prediction model us-ing big data in health care

Assessment of Risk of Type 2 Diabetes Mellitus with Stress as a Risk Factor using Classification Algorithms

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